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The era of pervasive AI has arrived. In this era, organizations will use generative AI to unlock hidden value in their data, accelerate processes, reduce costs, drive efficiency and innovation to fundamentally transform their businesses and operations at scale.

SambaNova Suite™ is the first full-stack, generative AI platform, from chip to model, optimized for enterprise and government organizations. Powered by the intelligent SN40L chip, the SambaNova Suite is a fully integrated platform, delivered on-premises or in the cloud, combined with state-of-the-art open-source models that can be easily and securely fine-tuned using customer data for greater accuracy. Once adapted with customer data, customers retain model ownership in perpetuity, so they can turn generative AI into one of their most valuable assets.

About The Role

The Principal Compiler Engineer - ML Systems position will be responsible for working with the different layers of the compiler stack and coordinating with other development teams here at SambaNova. It is a critical role responsible for driving innovation in compiler infrastructure and optimization algorithms that enable state-of-the-art ML model performance on the SambaNova platform. This can involve anything from digging through PyTorch and machine learning models to determining how to map operations on to our underlying hardware.

Responsibilities

Lead compiler engineering through ensuring standard methodologies, enterprise product insertion and process evolution.
Work with peers, domain experts, developers, customers, and work across the enterprise seeking optimal solutions.
Develop, integrate, and implement products.
Provide support for proposals in key areas aligned with core team competencies.

Basic Qualifications

Bachelor’s or Master’s Degree in Computer Science, Computer Engineering, or equivalent with 5-10 years of industry experience.

Additional Qualifications

Deep theoretical understanding of compiler fundamentals.
Experience building and deploying software products.
Experience with one or more deep learning frameworks (i.e. TensorFlow, PyTorch) is a plus.
Experience with common compiler development practices and methodologies.
Excitement about high-performance systems engineering and performance debugging.
An appreciation for process and developing cross-disciplinary collaboration.

Preferred Qualifications

Experience with MLIR.
Familiarity with machine learning models and frameworks.
Familiarity with accelerated computing.
Exposure to dataflow architectures.

Submission Guidelines
Please note that in order to be considered an applicant for any position at SambaNova Systems, you must submit an application form for each position for which you believe you are qualified.

EEO Policy
SambaNova Systems is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard basis of age (40 and over), color, disability, gender identity, genetic information, marital status, military or veteran status, national origin/ancestry, race, religion, creed, sex (including pregnancy, childbirth, breastfeeding), sexual orientation, and any other applicable status protected by federal, state, or local laws.

Benefits Summary for US-Based, Full-Time Employment Positions
SambaNova offers a competitive total rewards package, including the base salary, plus equity and benefits. We cover 95% premium coverage for employee medical insurance, and 77% premium coverage for dependents and offer a Health Savings Account (HSA) with employer contribution. We also offer Dental, Vision, Short/Long term Disability, Basic Life, Voluntary Life, and AD&D insurance plans in addition to Flexible Spending Account (FSA) options like Health Care, Limited Purpose, and Dependent Care. Our library of well-being benefits available to you and your dependents includes a full subscription to Headspace, Gympass+ membership with access to physical gyms, One Medical membership, counseling services with an Employee Assistance Program, and much more.

","salaryMinimum","salaryMaximum","salaryCurrency","salarySource","createdAt",["D",1772142469000],"closedAt",["D",1786196076028],"tags",[88],{"_65":85,"_67":86,"_69":87},"24252f83-edca-4d75-b448-d32a7b0b47b2","SambaNovaSystems","sambanova-ai",{"_89":90,"_91":92},"tag",{"_65":93,"_71":94,"_67":95,"_69":96,"_91":97},"priority",0,"d2e7f461-c2b7-4938-968d-d475cba595f6","location","Palo Alto, California","palo-alto-california",4000,{"_65":99,"_67":100,"_69":101,"_71":72,"_16":102,"_74":-5,"_75":-5,"_76":-5,"_77":-5,"_78":103,"_80":104,"_82":105,"_9":106},"fa7a90c0-57d5-4025-9e86-ae56fde0a80b","Security Architect – AI Silicon & Systems","security-architect-ai-silicon-systems-at-sambanova-ai-mar-3-2026-66e3d1","

Job Overview

SambaNova Systems is seeking a Security Architect to define and scale the security architecture for our AI silicon and systems. This role focuses on advancing hardware-rooted security, confidential computing, and secure AI infrastructure for enterprise and regulated markets. The ideal candidate has deep chip-level security expertise and is eager to collaborate across teams to address system-level challenges, building on our proven enterprise deployments.

Roles and Responsibilities

As a Security Architect at SambaNova, you will be responsible for leading the security architecture of our next-generation AI silicon and systems, ensuring they meet the demands of hyperscale and regulated environments. Key responsibilities include:

Conduct research and development in state-of-the-art security solutions for AI silicon, including hardware root-of-trust, secure boot, and confidential computing.
Define and own security architecture specifications for performant, robust, and efficient AI hardware and systems.
Lead cross-functional efforts with silicon, firmware, software, and system teams to integrate security into the product lifecycle.
Collaborate with executive leadership to align security initiatives with product competitiveness and market requirements.
Stay current with industry standards (e.g., Caliptra) and contribute to security advancements representing SambaNova.
Develop threat models and security protocols for AI accelerators and cluster environments.
Enhance secure deployment processes, including manufacturing, provisioning, and supply chain security.

Minimum Qualifications

Basic Qualifications

BS, MS, or PhD degree in Computer Science, Electrical Engineering, or a related field.
10+ years of industry experience in hardware security architecture, preferably in AI accelerators, GPUs, or high-performance SoCs.
Proven track record of delivering security solutions from architecture to product implementation.

Additional Required Qualifications

Knowledge and Skills

Deep understanding of hardware root-of-trust, secure boot, firmware security, and cryptography applied to silicon.
Hands-on experience with security technologies such as Caliptra for hardware security modules.
Ability to define and architect security features at the micro-architectural level, including memory protection, isolation, and key management.
Strong analytical and debug skills to solve challenging security problems in dynamic environments.
Capable of breaking down security requirements into silicon, firmware, and system software specifications.

Leadership and Teamwork

Highly motivated, self-driven, and independent, with strong organizational skills.
Excellent collaboration and communication skills to work effectively with cross-functional teams.
Willingness to learn and adapt to new areas with mentorship and collaboration.

Preferred Qualifications

15+ years of industry experience in hardware security for AI, supercomputing, or hyperscale environments.
Experience with confidential computing architectures and industry standards.
Knowledge of system-level security integration, including BMC, network, and software security (e.g., Docker, Kubernetes).
Familiarity with off-the-shelf security IP and optical switch security considerations.
Experience in conducting trade-off analyses on security vs. performance/TCO for AI systems.
Background in leading cross-functional teams or contributing to industry security workgroups.

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About the Role

SambaNova is hiring Software Engineers for SambaNova’s SambaStack platform.

We are helping enterprises and service providers host their own AI inference platforms for end users, powered by our state-of-the-art RDU (Reconfigurable Dataflow Unit) hardware architecture. Our cloud-agnostic, enterprise-grade inference serving platform enables seamless deployment, management, and scaling of foundation model workloads at production scale.

As a Software Engineer on the SambaStack team, you will further improve performance, scalability, and reliability while introducing new features and capabilities to our state-of-the-art inference serving platform.

This is a high-impact, high-visibility role working at the intersection of AI infrastructure, enterprise software, and developer experience.

Key Responsibilities

Ensure the continued stability, scalability, and performance of our AI inference platform.
Collaborate with ML Engineers, Product Managers, QA, and DevOps to build the best system for our customers.
Work on components built with Python, Go, and Rust across a heterogeneous serving platform.
Build and improve testing frameworks and automation to increase our ability to quickly deliver high quality releases to customers.
Design new systems, components, and capabilities to solve new and interesting problems in the AI inference space.

Required Qualifications

4+ years of experience in Software Engineering.
Experience building backend components in Python, Go, or Rust.
Experience with unit, integration, and end-to-end testing framework design and implementation.
Strong understanding of Kubernetes and the Kubernetes Operator pattern.
Excellent problem-solving skills and attention to detail
Ability to work collaboratively with cross-functional teams
Knowledge of software development best practices and coding standards

Preferred Qualifications

Familiarity with AI Inference serving and orchestration.
Familiarity with AI coding assistants and technologies.
Knowledge of ML application optimization and productionization.
Ability to design and build cloud-native software systems.

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About the Role

We are seeking a Principal AI Solutions Engineer to join our Product and Solution Engineering team, driving product adoption through advanced AI solutions development, agentic AI implementation, and performance benchmarking. This role combines deep technical expertise with customer-facing responsibilities, supporting our Customer Driven Product Program (CDPP) and Product QA initiatives.

This senior technical role provides customer-facing architecture leadership essential for scaling SambaNova's enterprise adoption:

Customer Reference Architectures: Design and document production-ready patterns for enterprise AI deployments
SambaStack Tooling: Develop tooling and automation for SambaStack deployment and management
Agentic AI Solutions: Build customer-facing agentic solutions that accelerate time-to-value
US Timezone Coverage: Enable global coordination with customers and partners across time zones

Impact: Senior technical leadership for customer-facing architecture work. Drives adoption through reference implementations and best practices documentation.

Responsibilities

AI Solutions Development: Develop robust, efficient, and scalable AI solutions for enterprise customers
Agentic AI Implementation: Lead the design and implementation of agentic AI systems and multi-agent architectures
Performance Benchmarking: Conduct comprehensive performance benchmarking and optimization of AI models and systems
Customer Engagement: Lead technical enablement for CDPP customers through workshops and hands-on implementation sessions
Model Integration: Integrate latest model architectures into SambaNova's platform stack
End-to-End Solutions: Create complete ML application solutions from concept to production deployment
Inference Optimization: Enable high-throughput, low-latency inference deployments for customer workloads
Cross-Functional Leadership: Collaborate with product and engineering teams to drive platform improvements
Technical Thought Leadership: Contribute to reference architectures, best practices, and technical documentation

Required Qualifications

Bachelor's degree or higher in Computer Science, Electrical Engineering, Applied Mathematics, Physics, or Statistics
5+ years of industry experience in software engineering or ML engineering
3+ years of hands-on experience with deep learning and machine learning systems
Proficiency in Python or C++ programming
Experience with modern ML frameworks:
PyTorch, TensorFlow, or JAX
Real-world experience deploying LLM-based applications
Strong client communication and presentation skills
Experience with benchmarking methodologies and performance optimization

Preferred Qualifications

Experience with multi-modal AI systems (vision, audio, text)
Hands-on experience with distributed training/inference frameworks:
vLLM, DeepSpeed, Megatron-LM
Experience building agentic AI systems (LangChain, LangGraph, CrewAI, etc.)
Knowledge of enterprise AI deployment patterns
Experience with technical partnerships and customer-facing roles

What We Offer

Lead cutting-edge AI solutions for enterprise customers
Work with the fastest AI inference platform in the industry
Global team collaboration with exposure to diverse customer challenges
Opportunity to shape AI adoption across enterprise markets
Competitive compensation and benefits

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About SambaNova Systems

Join the company that's building the future of AI computing. At SambaNova, we are disrupting the AI and high-performance computing space with our integrated hardware and software platform. Our DataScale systems and SambaFlow software are pushing the boundaries of what's possible with generative AI and large language models. We are a team of passionate innovators tackling some of the world's most challenging computational problems.

The Role

As a Senior Cloud Site Reliability Engineer (SRE) specializing in our AI Inferencing Service, you will be the guardian of its reliability, performance, and scalability. You will bridge the gap between software development and operations, applying an engineering mindset to solve operational challenges. Your primary focus will be ensuring our inference endpoints have exceptional uptime, low-latency response times, and efficient resource utilization, directly impacting the experience of our customers and the success of our AI products. This role includes participating in a shared on-call rotation to maintain 24/7 service reliability.

What You'll Do

Service Ownership & On-Call: Take shared ownership of the production inferencing service, including its availability, latency, performance, efficiency, change management, monitoring, emergency response, and capacity planning across multiple regions. This includes implementing and supporting AI infrastructure in new regions, such as Asia, Europe, and Latin America, to support the growth of our business. Participate in a balanced on-call rotation to provide 24/7 support for the service.

On-Call & Work-Life Balance

We believe a sustainable on-call schedule is critical for long-term success and team health. Our on-call philosophy is built on the following principles:

Balanced Rotation: The on-call rotation is shared equally across the team, typically following a primary/secondary (follow-the-sun) model to ensure no single person bears a disproportionate burden.
Focus on Prevention: We invest heavily in automation, robust testing, and system design to prevent pages before they happen. The goal of on-call is not to heroically fight fires, but to manage rare, complex failures and use those learnings to make the system more resilient.
Actionable Alerts: We have a strict policy against alert fatigue. Alerts must be actionable and require immediate human intervention.
Incident Management: Lead the response to incidents affecting the inferencing service, driving blameless post-mortems and implementing corrective actions to prevent recurrence.
Monitoring & Alerting: Develop and maintain advanced monitoring, alerting, and dashboarding (using tools like Prometheus, Grafana, Datadog) to gain deep insights into service health, model performance (e.g., latency, throughput, error rates), and accelerator utilization. A key responsibility is ensuring alerts are actionable and have a low false-positive rate, minimizing on-call fatigue.
Performance & Scalability: Proactively identify and eliminate performance bottlenecks. Design and implement auto-scaling policies to handle variable inference loads cost-effectively. Use insights from on-call incidents to drive improvements that enhance system stability and scalability.
Infrastructure as Code (IaC): Manage and evolve our cloud infrastructure (on AWS, GCP, and/or Azure along with on-prem) using tools like Terraform and Ansible, ensuring it is secure, repeatable, and scalable.
CI/CD & Automation: Champion automation by building and improving CI/CD pipelines for the seamless and safe deployment of new model versions and service updates. A core goal is to automate manual toil identified during on-call shifts, reducing future operational overhead.
Capacity Planning: Forecast infrastructure needs based on product roadmaps and usage trends. Work with finance and engineering teams to manage cloud costs and optimize spending.
SLOs & SLIs: Define, measure, and report on Service Level Objectives (SLOs) and Indicators (SLIs) for the inferencing platform, using data to drive prioritization and reliability investments.

What We're Looking For (Must-Haves)

Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
5-8+ years of experience in a Site Reliability Engineer, DevOps, or related role supporting a large-scale, customer-facing service in a public cloud environment (AWS, GCP, Azure).
Strong programming/scripting skills in languages like Python, Go, or Java.
Proven experience with containerization and orchestration technologies (Docker, Kubernetes).
Deep understanding of monitoring and observability principles and tools (e.g., Prometheus, Grafana, ELK Stack, Datadog).
Solid experience with Infrastructure as Code (e.g., Terraform, CloudFormation).
Familiarity with CI/CD principles and tools (e.g., Jenkins, GitHub Actions, ArgoCD).
Excellent problem-solving skills and a systematic approach to troubleshooting complex distributed systems.

What Will Make You Stand Out (Nice-to-Haves)

Experience in a hybrid environment bridging cloud and on-premise/data center infrastructure.
Direct experience supporting ML/AI inferencing services in production.
Familiarity with GPU-accelerated computing and optimizing workloads for NVIDIA GPUs for purposes of mapping to RDUs.
Knowledge of model serving frameworks like vLLM, SGLang or Ray.
Understanding of MLOps principles and practices.
Experience with managing and tuning databases (SQL or NoSQL) and caching systems (Redis, Memcached).
Strong Linux/Unix system administration fundamentals.

Why SambaNova?

Massive Impact: You will be a key part of a critical platform with high visibility and direct impact on our product and engineers.
Cutting-Edge Technology: Work with a world-class team on one of the most advanced AI stacks in the industry.
Autonomy and Growth: We trust you to make technical decisions. This is a greenfield opportunity to build something remarkable from the ground up.
Competitive Compensation: Including equity, excellent benefits, and a flexible work environment.

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About The Role

Responsibilities

Lead compiler engineering through ensuring standard methodologies, enterprise product insertion and process evolution.
Work with peers, domain experts, developers, customers, and work across the enterprise seeking optimal solutions.
Develop, integrate, and implement products.
Provide support for proposals in key areas aligned with core team competencies.

Basic Qualifications

Bachelor’s or Master’s Degree in Computer Science, Computer Engineering, or equivalent with 5-10 years of industry experience.

Additional Qualifications

Deep theoretical understanding of compiler fundamentals.
Experience building and deploying software products.
Experience with one or more deep learning frameworks (i.e. TensorFlow, PyTorch) is a plus.
Experience with common compiler development practices and methodologies.
Excitement about high-performance systems engineering and performance debugging.
An appreciation for process and developing cross-disciplinary collaboration.

Preferred Qualifications

Experience with MLIR.
Familiarity with machine learning models and frameworks.
Familiarity with accelerated computing.
Exposure to dataflow architectures.

",["D",1763771736000],[279,280],{"_65":85,"_67":86,"_69":87},{"_89":282,"_91":124},{"_89":281,"_91":92},{"_65":93,"_71":94,"_67":95,"_69":96,"_91":97},{"_65":189,"_71":126,"_67":190,"_69":191,"_91":129},{"_65":284,"_67":285,"_69":286,"_71":72,"_16":287,"_74":-5,"_75":-5,"_76":-5,"_77":-5,"_78":288,"_80":-5,"_82":289,"_9":290},"b05731b2-6127-4a41-b7c3-59fdee8c5e7d","ML Features Solutions Engineer","ml-features-solutions-engineer-at-sambanova-ai-mar-4-2026-036dbd","

About the Role

We are seeking an ML Features Solutions Engineer to join our Product and Solution Engineering team, driving the development and optimization of core ML features for enterprise deployment. This role combines deep ML expertise with hands-on engineering, working at the intersection of ML research and product development to deliver production-grade capabilities to our customers.

This role is critical for accelerating ML feature development and bridging the gap between ML research and product engineering and will be driving the following:

Core ML Feature Development: Drive improvements to ML features including model optimization, inference performance, and feature enhancements.
Production-Ready Solutions: Build and deploy production-ready ML solutions for enterprise customers with focus on reliability and scale.
Research to Product Bridge: Translate ML research innovations into practical product features and customer-facing capabilities.
Cross-Team Collaboration: Work closely with SDK, testing, and customer teams to ensure ML features meet enterprise requirements.
Impact: Accelerates ML feature development and optimization, enabling faster time-to-market for new capabilities while ensuring enterprise-grade quality and performance.

Responsibilities

Design and implement core ML features including model optimization, quantization, and inference enhancements
Optimize model performance for latency, throughput, and memory efficiency on SambaNova hardware
Develop and improve features such as Function Calling, Structured Output, and JSON mode conformance
Create end-to-end ML solutions that showcase platform capabilities and accelerate customer adoption
Convert cutting-edge ML research into practical, deployable product features
Establish benchmarks and quality standards for ML features in production environments
Work with SDK team to ensure ML features are properly exposed and documented for developers
Support enterprise customers implementing advanced ML features in their workflows
Partner with ML research, platform engineering, and customer teams

Required Qualifications

Master’s degree or higher in Computer Science, Machine Learning, Electrical Engineering, or related field
5+ years of industry experience in ML engineering or applied ML research
3+ years of hands-on experience with large language models and transformer architectures
Expert proficiency in Python and deep learning frameworks: PyTorch (required), TensorFlow, or JAX
Experience with model optimization techniques: quantization, pruning, distillation, efficient inference
Strong understanding of LLM inference optimization: KV cache, batching strategies, memory management
Experience deploying ML models to production at scale
Track record of translating research concepts into production features

Preferred Qualifications

PhD in Machine Learning, NLP, or related field
Experience with custom hardware acceleration (TPUs, custom ASICs)
Hands-on experience with inference frameworks: vLLM, TensorRT-LLM, or similar
Experience with function calling and tool use in LLMs
Knowledge of structured generation and constrained decoding
Experience with ML feature development in enterprise contexts
Contributions to open-source ML projects

What We Offer

Work on cutting-edge ML features powering the fastest AI inference platform
Direct impact on product capabilities used by enterprise customers globally
Collaborate with world-class ML researchers and engineers
Bay Area location enabling close collaboration with core ML teams
Competitive compensation and benefits
Opportunity to shape the future of enterprise AI

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The Opportunity

The Runtime team at Sambanova is a seasoned engineering team with a proven track record of delivering cutting-edge system software solutions for AI and machine learning applications in the enterprise & commercial landscape.

Runtime is responsible for the lowest levels of the SambaNova stack, efficiently interacting with the hardware to provide the best application performance and maximize hardware utilization. We handle all aspects of software infrastructure to enable higher level applications, including:

High performance user libraries
Operating System interface/integration
Data model manipulation for scaling
Networking/communication intra and inter node
Orchestration of partitioned workloads
Error monitoring and tools for system management and observability

We build a high performance, distributed and scalable software execution environment for SambaNova DataScale & Cloud platforms to support data-flow applications such as ML training and inference and HPC applications.

We are searching for a software engineer who will work on all parts of the runtime stacks, supporting AI, ML, and scientific applications in high-performance distributed systems. You will participate in building, testing and deploying next-generation high-performance compute systems for AI applications at scale. We expect the candidate to have a strong background in programming, building and testing software in distributed systems, performance tuning of large scale systems, and good teamwork and planning skills.

Role Responsibilities

Work on design and implementation of new and enhanced features of the runtime stack to support high performance and scalable ML inference and training applications
System software (drivers and kernel) support for the next generation silicon.
Design user-space libraries for high performance and high utilization of HW resources.
User-facing tools (analysis, job and HW management, profiling, debugging, etc) for Datascale systems.
Collaborate with other teams including Hardware, ML Application, Compiler, DevOps.

Basic Qualifications

Bachelor’s in Computer Science, Computer Engineering, or equivalent and with 3-5 years of industry experience
Proficiency in C/C++ and Python
Experience with user space libraries, operating systems, and kernel drivers
Experience working with highly concurrent and distributed systems, with a focus on performance and scalability

Preferred Qualifications

Experience with different types of fabrics, such as PCIe, Infiniband, and RoCE
Experience with fast networking stacks, such as RDMA
Good communication skills and enthusiasm to help colleagues

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Job Summary

We are seeking a highly capable and dynamic Technical Program Manager (TPM) to drive product delivery at Sambanova Systems. You will play a critical role in planning, driving execution, and orchestrating multiple workstreams and dependencies to drive engineering velocity and transparency towards delivering customer product releases

Responsibilities

Lead various Software programs and initiatives in a fast moving organization across different locations covering a range of areas.
Work closely with engineers, product managers and other stakeholders to define and prioritize Product features and capabilities.
Put together a comprehensive plan that encompasses all the diverse efforts that are in motion within the teams and streamline the program to improve visibility and scalability.
Provide hands-on support from development to deployment, including requirements gathering, managing scope, schedule tracking, qualitative testing and documentation.
Manage cross functional dependencies, risks and changes effectively across multiple streams.
Leverage your excellent communication and coordination skills to bring the best out of multiple teams. You're equally comfortable explaining your team's analyses and recommendations to executives as you are discussing the technical tradeoffs with engineers.
Identify and drive ongoing process improvement opportunities.

Qualifications

Bachelor’s or Master’s degree in Computer Science or a related field
8+ years of experience in technical program management
3+ years of experience in Software or ML development

Preferred Qualifications

Experience in launching Software products from research to production.
Ability to work independently and successfully in a fast paced nascent environment.
Experience with roadmap planning, forecasting and release process management.
Adept at working with highly skilled and independent engineers.
Self-motivated and willing to take additional responsibilities over time to contribute to the team’s success.

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Overview

SambaNova is seeking a Principal Engineer to own post-silicon bring-up, debug, and validation of high-speed IO and memory subsystems within the DataScale® platform. This role is central to delivering reliable, high-performance AI systems at scale. You will lead complex system-level debug across silicon, board, and software layers, driving issues to root cause and resolution from first silicon through deployment. This is a hands-on role for an engineer with deep expertise in high-speed interfaces and hardware debug, who can operate in ambiguity and execute with high ownership.

Responsibilities

System Bring-Up & Validation

Own post-silicon bring-up and validation of high-speed IO and memory subsystems, including characterization, functional validation, stress testing, and performance analysis
Enable system power-on and bring-up readiness, including debug infrastructure, tooling, and contingency planning

Debug & Cross-Functional Execution

Lead system-level debug to root cause complex issues spanning silicon, board, and software layers
Drive cross-functional resolution with design, firmware, verification, and system teams to ensure timely bring-up and issue closure

Productization & Deployment

Support product validation, deployment, and RMA debug in partnership with system and customer engineering teams
Ensure readiness for product release with a focus on stability, performance, and field reliability

Pre-Silicon & Design Feedback

Review SoC, board design, and SI/PI analysis from a post-silicon debug and deployment perspective
Contribute to pre-silicon verification and emulation to improve bring-up efficiency and coverage

Infrastructure & Methodology

Develop scripts and tools to automate validation, debug, and data analysis workflows
Improve post-silicon validation methodologies, debug infrastructure, and test scalability

Basic Qualifications

MS in Electrical Engineering or Computer Science
7+ years of experience in post-silicon validation or system bring-up

Additional Required Qualifications

Experience with high-speed IO or memory protocols such as PCIe, Ethernet, hBM, CXL, or UCIe
Strong analytical skills for test development and data analysis
Proficiency in scripting (e.g., bash, python)
Proven ability to lead cross-functional debugging efforts
Self-starter with strong ownership and execution skills
Clear and effective communicator with a proactive, collaborative approach to problem-solving

Preferred Qualifications

Deep understanding of high-speed SerDes training and adaptation
Experience with mixed-signal IO design and validation
Hands-on experience with lab equipment (e.g., oscilloscopes)
Proficiency in programming languages, such as C,

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About the Role

SambaNova’s DataScale® platform pushes the limits of AI compute—and the integrity of every high-speed interface and memory subsystem is critical to its performance in the field. As a Principal Engineer, Post-Silicon Validation, you will own the bring-up, debug, and validation of high-speed IO and memory subsystems from first silicon through customer deployment.

This is a deeply technical, high-ownership role for an engineer who thrives on hard debug problems. You will operate across silicon, board, and software layers—leading cross-functional teams to root cause, driving issues to resolution, and building the infrastructure that makes future bring-up cycles faster and more reliable. If you have a bias for action, a sharp analytical mind, and the kind of depth that comes from years in post-silicon validation, this role was built for you.

What You’ll Do

System Bring-Up & Validation

Own post-silicon bring-up and validation of high-speed IO and memory subsystems, including characterization, functional validation, stress testing, and performance analysis.
Drive system power-on readiness—including debug infrastructure, tooling setup, and contingency planning—to enable first-pass bring-up success.

Debug & Cross-Functional Execution

Lead system-level debug across silicon, board, and software layers, driving complex issues to verified root cause and resolution.
Partner with design, firmware, verification, and systems teams to ensure timely bring-up and issue closure—without losing momentum.

Productization & Deployment

Support product validation, deployment, and RMA debug in partnership with systems and customer engineering teams.
Ensure product release readiness with a clear focus on stability, performance, and long-term field reliability.

Pre-Silicon & Design Feedback

Review SoC architecture, board design, and SI/PI analysis through the lens of post-silicon debug and real-world deployment.
Contribute to pre-silicon verification and emulation to improve bring-up efficiency and test coverage before first silicon arrives.

Infrastructure & Methodology

Develop scripts and tooling to automate validation, debug, and data analysis workflows—reducing manual effort and accelerating iteration.
Drive continuous improvement in post-silicon validation methodologies, debug infrastructure, and test scalability across product generations.

What You’ll Bring

Required

M.S. in Electrical Engineering or Computer Science.
7+ years of experience in post-silicon validation, system bring-up, or a closely related hardware engineering discipline.
Hands-on experience with high-speed IO or memory protocols: PCIe, Ethernet, HBM, CXL, UCIe, or similar.
Strong analytical skills applied to test development and data-driven debug.
Scripting proficiency in Python, Bash, or equivalent.
Demonstrated ability to lead cross-functional debugging efforts across hardware, firmware, and software teams.
High ownership, self-starter mindset—you drive issues to closure without waiting to be asked.
Clear and effective communicator with a proactive, collaborative approach to problem-solving.

Preferred

Deep expertise in high-speed SerDes training, adaptation, and equalization.
Experience with mixed-signal IO design and validation.
Hands-on proficiency with lab equipment including high-bandwidth oscilloscopes, BERTs, and VNAs.
Programming experience in C or C++ for tooling, automation, or low-level debug.

Why SambaNova Systems

At SambaNova, you’ll work on some of the most complex silicon and systems being built today—at the frontier of AI compute. Our post-silicon validation team plays a defining role in every product we ship: the depth of your work here has a direct impact on what our customers can do with AI. You’ll collaborate with world-class engineers across silicon design, systems, and software, with the resources and urgency that come with being at the leading edge of the industry.

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About SambaNova

Join the company that's building the future of AI computing. SambaNova is disrupting the AI and high-performance computing space with an integrated hardware and software platform.

Our SambaStack inference serving platform is pushing the boundaries of inference serving for generative AI and large language models. We are a team of passionate innovators tackling some of the world's most challenging computational problems.

About the Role

SambaNova is hiring a Software Architect for the SambaStack platform.

We help enterprises and service providers host their own AI inference platforms, powered by our state-of-the-art RDU (Reconfigurable Dataflow Unit) hardware architecture. Our cloud-agnostic, enterprise-grade inference serving platform enables seamless deployment, management, and scaling of foundation model workloads at production scale.

As a Software Architect on the SambaStack team, you will define the technical direction of the platform, shaping how it evolves to meet the demands of the world's most demanding enterprise AI workloads. You will own the cross-cutting architectural decisions that span teams and shape the platform's long-term trajectory.

This is a high-impact, high-visibility role at the intersection of:

AI infrastructure
Enterprise software
Systems architecture and design
Developer experience

Key Responsibilities

Own the end-to-end technical architecture of the SambaStack inference platform, ensuring it meets enterprise-grade standards for scalability, reliability, and performance.
Define and drive architectural strategy across engineering teams, aligning long-term platform direction with product and business objectives.
Lead design reviews for major new capabilities and cross-cutting system changes, providing authoritative technical guidance and raising the quality bar across the SambaStack engineering organization.
Proactively identify and resolve systemic risks, including performance bottlenecks, reliability gaps, and scaling constraints, before they impact customers.
Establish and evolve architectural standards, patterns, and best practices across the SambaStack engineering organization.
Partner with ML Engineers, Product Managers, QA, and DevOps to translate complex, ambiguous requirements into sound architectural decisions.
Serve as a technical thought leader and mentor, growing the architectural thinking and design skills of Software Engineers across the team.
Evaluate emerging technologies and industry trends to inform the platform's technical roadmap.
Architect new systems, components, and capabilities to solve new and interesting problems in the AI inference space.

Required Qualifications

12+ years of software engineering experience, including significant time in a role with organization-wide technical influence.
Proven track record of owning large-scale distributed systems architectures end-to-end from design to production.
Expertise building backend systems in Python, Go, or Rust.
Expert-level understanding of Kubernetes, including Operators, Helm Charts, and cluster-scale resource management.
Strong command of distributed systems principles: consistency, fault tolerance, observability, and performance at scale.
Demonstrated experience setting technical direction across multiple teams and driving cross-functional alignment.
Excellent written and verbal communication skills: Able to clearly articulate complex architectural decisions to both engineering and non-technical audiences.
Track record of mentoring more junior engineers and elevating team-wide engineering quality.

Preferred Qualifications

Deep familiarity with AI inference serving, orchestration, and the infrastructure challenges of LLM workloads at production scale.
Experience designing systems for custom or novel hardware accelerators (e.g., dataflow architectures, GPUs, TPUs).
Background in ML application optimization and productionization.
Experience building cloud-native platforms for enterprise customers with stringent SLA and compliance requirements.
Familiarity with AI coding assistants and the developer tooling ecosystem.
Experience at a high-growth AI infrastructure company.

Base Salary Range:

Base Pay Range

$245,000—$325,000 USD

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About SambaNova Systems: Join the company that's building the future of AI computing. At SambaNova, we are disrupting the AI and high-performance computing space with our integrated hardware and software platform. Our DataScale systems and SambaFlow software are pushing the boundaries of what's possible with generative AI and large language models. We are a team of passionate innovators tackling some of the world's most challenging computational problems.

The Role

As a Cloud Site Reliability Engineer (SRE) specializing in our AI Inferencing Service, you will be the guardian of its reliability, performance, and scalability. You will bridge the gap between software development and operations, applying an engineering mindset to solve operational challenges. Your primary focus will be ensuring our inference endpoints have exceptional uptime, low-latency response times, and efficient resource utilization, directly impacting the experience of our customers and the success of our AI products. This role includes participating in a shared on-call rotation to maintain 24/7 service reliability.

What You'll Do:

On-Call & Work-Life Balance

We believe a sustainable on-call schedule is critical for long-term success and team health. Our on-call philosophy is built on the following principles:

Balanced Rotation: The on-call rotation is shared equally across the team, typically following a primary/secondary (follow-the-sun) model to ensure no single person bears a disproportionate burden.

Focus on Prevention: We invest heavily in automation, robust testing, and system design to prevent pages before they happen. The goal of on-call is not to heroically fight fires, but to manage rare, complex failures and use those learnings to make the system more resilient.

Actionable Alerts: We have a strict policy against alert fatigue. Alerts must be actionable and require immediate human intervention.

Incident Management: Lead the response to incidents affecting the inferencing service, driving blameless post-mortems and implementing corrective actions to prevent recurrence.

Monitoring & Alerting: Develop and maintain advanced monitoring, alerting, and dashboarding (using tools like Prometheus, Grafana, Datadog) to gain deep insights into service health, model performance (e.g., latency, throughput, error rates), and accelerator utilization. A key responsibility is ensuring alerts are actionable and have a low false-positive rate, minimizing on-call fatigue.

Performance & Scalability: Proactively identify and eliminate performance bottlenecks. Design and implement auto-scaling policies to handle variable inference loads cost-effectively. Use insights from on-call incidents to drive improvements that enhance system stability and scalability.

Infrastructure as Code (IaC): Manage and evolve our cloud infrastructure (on AWS, GCP, and/or Azure along with on-prem) using tools like Terraform and Ansible, ensuring it is secure, repeatable, and scalable.

CI/CD & Automation: Champion automation by building and improving CI/CD pipelines for the seamless and safe deployment of new model versions and service updates. A core goal is to automate manual toil identified during on-call shifts, reducing future operational overhead.

Capacity Planning: Forecast infrastructure needs based on product roadmaps and usage trends. Work with finance and engineering teams to manage cloud costs and optimize spending.

SLOs & SLIs: Define, measure, and report on Service Level Objectives (SLOs) and Indicators (SLIs) for the inferencing platform, using data to drive prioritization and reliability investments.

What We're Looking For (Must-Haves):

Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

3-5+ years of experience in a Site Reliability Engineer, DevOps, or related role supporting a large-scale, customer-facing service in a public cloud environment (AWS, GCP, Azure).

Strong programming/scripting skills in languages like Python, Go, or Java.

Proven experience with containerization and orchestration technologies (Docker, Kubernetes).

Deep understanding of monitoring and observability principles and tools (e.g., Prometheus, Grafana, ELK Stack, Datadog).

Solid experience with Infrastructure as Code (e.g., Terraform, CloudFormation).

Familiarity with CI/CD principles and tools (e.g., Jenkins, GitHub Actions, ArgoCD).

Excellent problem-solving skills and a systematic approach to troubleshooting complex distributed systems.

What Will Make You Stand Out (Nice-to-Haves):

Experience in a hybrid environment bridging cloud and on-premise/data center infrastructure.

Direct experience supporting ML/AI inferencing services in production.

Familiarity with GPU-accelerated computing and optimizing workloads for NVIDIA GPUs for purposes of mapping to RDUs.

Knowledge of model serving frameworks like vLLM, SGLang or Ray.

Understanding of MLOps principles and practices.

Experience with managing and tuning databases (SQL or NoSQL) and caching systems (Redis, Memcached).

Strong Linux/Unix system administration fundamentals.

Why SambaNova?

Massive Impact: You will be a key part of a critical platform with high visibility and direct impact on our product and engineers.

Cutting-Edge Technology: Work with a world-class team on one of the most advanced AI stacks in the industry.

Autonomy and Growth: We trust you to make technical decisions. This is a greenfield opportunity to build something remarkable from the ground up.

Competitive compensation, including equity, excellent benefits, and a flexible work environment.

How to Apply: If you are a versatile engineer who loves to build scalable systems and wants to use those skills to power the future of AI, we want to hear from you.

Base Salary Range:

Base Pay Range

₹6,000,000—₹8,000,000 INR

",["D",1785879598000],[407,408,409,410,411,412,413,414,415,416,417,418,419,420,421],{"_65":85,"_67":86,"_69":87},{"_89":436,"_91":92},{"_89":435,"_91":114},{"_89":434,"_91":114},{"_89":433,"_91":114},{"_89":432,"_91":157},{"_89":431,"_91":114},{"_89":430,"_91":114},{"_89":429,"_91":114},{"_89":428,"_91":114},{"_89":427,"_91":114},{"_89":426,"_91":114},{"_89":425,"_91":114},{"_89":424,"_91":157},{"_89":423,"_91":157},{"_89":422,"_91":114},{"_65":115,"_71":116,"_67":117,"_69":118,"_91":114},{"_65":224,"_71":116,"_67":225,"_69":226,"_91":157},{"_65":158,"_71":116,"_67":159,"_69":160,"_91":157},{"_65":229,"_71":116,"_67":230,"_69":231,"_91":114},{"_65":237,"_71":116,"_67":238,"_69":239,"_91":114},{"_65":120,"_71":116,"_67":121,"_69":122,"_91":114},{"_65":242,"_71":116,"_67":243,"_69":244,"_91":114},{"_65":246,"_71":116,"_67":247,"_69":248,"_91":114},{"_65":250,"_71":116,"_67":251,"_69":252,"_91":114},{"_65":254,"_71":116,"_67":255,"_69":256,"_91":114},{"_65":167,"_71":116,"_67":168,"_69":169,"_91":157},{"_65":259,"_71":116,"_67":260,"_69":261,"_91":114},{"_65":263,"_71":116,"_67":264,"_69":265,"_91":114},{"_65":267,"_71":116,"_67":268,"_69":269,"_91":114},{"_65":437,"_71":94,"_67":438,"_69":439,"_91":97},"88b2585f-47a5-48e1-9f42-835748b613af","Bengaluru, Karnataka","bengaluru-karnataka",{"_65":441,"_67":442,"_69":443,"_71":72,"_16":444,"_74":445,"_75":446,"_76":379,"_77":380,"_78":447,"_80":-5,"_82":448,"_9":449},"8f121a62-0522-46cf-8c9e-7e748f76fcbc","Senior Software Engineer - ML Infrastructure","senior-software-engineer-ml-infrastructure-at-sambanova-ai-aug-5-2026-fe017d","

Overview

The Senior Software Engineer, ML Infrastructure will be responsible for designing, building, and operating the production-grade inference infrastructure that powers SambaNova's serving stack on our Reconfigurable Dataflow Unit (RDU) architecture. SambaNova is an inference-first company, and this role sits at the heart of that mission: turning state-of-the-art inference techniques into reliable, high-throughput, low-latency services exposed to customers through SambaStack and SambaCloud. The engineer will own end-to-end systems spanning request scheduling, advanced decoding algorithms, caching layers, API surfaces, and the accuracy infrastructure that keeps the stack trustworthy. This role partners closely with ML, compiler, runtime, and product teams to ship inference features from prototype to production.

Qualifications

Bachelor's degree in Computer Science, Electrical Engineering, or related field
5+ years of industry experience building and operating large-scale distributed systems, ideally in ML serving
Strong software engineering fundamentals: algorithms, data structures, concurrency, and systems design
Experience designing and maintaining production services with strict latency, throughput, and availability requirements
Working knowledge of modern LLM inference techniques and familiarity with open-source serving stacks such as vLLM, TensorRT-LLM, or SGLang
Proficiency in Python
Experience collaborating across teams to deliver complex, system-level engineering solutions

Key responsibilities

Design and productionize advanced inference techniques on RDU to optimize for performance and cost. Key areas include speculative decoding, constrained decoding, function/tool calling, prompt caching, and long-context inference.
Own SambaNova's integration with vLLM and adjacent serving frameworks, adapting them to RDU's architecture.
Own the public inference API surface exposed through SambaStack and SambaCloud.
Build and maintain the accuracy verification and regression infrastructure that gates every inference feature shipped to customers.
Partner with ML, compiler, runtime, and product teams to take inference features from prototype to production.
Contribute to technical design discussions, code reviews, and architectural decisions as a senior individual contributor.

Base Salary Range:

Base Pay Range

$200,000—$275,000 USD

",200000,275000,["D",1785947681000],[450,451,452,453,454],{"_65":85,"_67":86,"_69":87},{"_89":463,"_91":92},{"_89":462,"_91":92},{"_89":461,"_91":157},{"_89":456,"_91":457},{"_89":455,"_91":124},{"_65":233,"_71":126,"_67":234,"_69":235,"_91":129},{"_65":458,"_71":116,"_67":459,"_69":460,"_91":457},2400,"29408dbd-4f21-4377-8233-57fcd6bd65c3","API","api",{"_65":167,"_71":116,"_67":168,"_69":169,"_91":157},{"_65":135,"_71":94,"_67":136,"_69":137,"_91":97},{"_65":139,"_71":94,"_67":140,"_69":141,"_91":92},{"_65":465,"_67":466,"_69":467,"_71":72,"_16":468,"_74":377,"_75":378,"_76":379,"_77":380,"_78":469,"_80":-5,"_82":470,"_9":471},"e0bb00a2-56f1-407d-95d5-0669a1cdb804","Software Engineering Director - Inference Platform","software-engineering-director-inference-platform-at-sambanova-ai-aug-5-2026-002e0b","

About the Role

We help enterprises and service providers host their own AI inference platforms, powered by our state-of-the-art RDU (Reconfigurable Dataflow Unit) hardware architecture. Our cloud-agnostic, enterprise-grade inference serving platform enables seamless deployment, management, and scaling of foundation model workloads at production scale.

As an Engineering Director on the SambaStack team, you will lead a high-performing team of engineers while remaining a hands-on technical contributor. You will be responsible for the growth and effectiveness of your team, the technical quality of their output, and driving key engineering initiatives while continuing to contribute directly on the platform's most important challenges.

This is a high-impact, high-visibility role combining:

People leadership and team development
AI infrastructure
Enterprise software
Systems architecture and design

Responsibilities

People & Team Leadership

Lead, grow, and inspire a software team, fostering a culture of technical excellence, collaboration, and continuous improvement
Own hiring for your team: define roles, assess candidates, and build a diverse, high-performing engineering organization
Drive the career development of your engineers through regular 1:1s, meaningful feedback, and clear growth plans
Set clear goals and expectations, track team progress, and remove blockers to keep the team executing effectively
Partner with Product, ML, QA, and DevOps leadership to align team priorities with broader organizational objectives

Technical Contribution

Contribute directly as a software engineer: designing systems, writing code, and leading implementation on critical platform initiatives
Participate in and lead design reviews, providing authoritative technical guidance on major features and cross-cutting changes
Establish and uphold engineering standards, best practices, and coding quality across the team
Proactively identify and drive the resolution of systemic risks: performance bottlenecks, reliability gaps, and scaling constraints
Stay current on emerging technologies and AI inference trends to inform the team's technical direction

Required Qualifications

12+ years of software engineering experience, including 2+ years leading and managing engineering teams
Demonstrated ability to manage, develop, and hire strong engineers while remaining a hands-on technical contributor
Strong technical depth in backend systems built with Python, Go, or Rust
Experience with AI inference serving, orchestration, and the infrastructure challenges of LLM workloads at production scale
Proven ability to drive engineering excellence across an engineering team
Excellent communication skills: able to work effectively with engineers, cross-functional partners, and senior leadership
Experience running iterative engineering processes in a fast-paced environment

Preferred Qualifications

Experience with Kubernetes ecosystem tooling, including Operators and Helm Charts
Background in ML application optimization and productionization
Experience building or managing teams that deliver cloud-native platforms for enterprise customers
Familiarity with AI coding assistants and the developer tooling ecosystem
Experience at a high-growth AI infrastructure company

Base Salary Range:

Base Pay Range

$245,000—$325,000 USD

",["D",1785958529000],[472,473,474,475,476],{"_65":85,"_67":86,"_69":87},{"_89":481,"_91":157},{"_89":480,"_91":114},{"_89":479,"_91":157},{"_89":478,"_91":157},{"_89":477,"_91":92},{"_65":391,"_71":94,"_67":392,"_69":393,"_91":97},{"_65":158,"_71":116,"_67":159,"_69":160,"_91":157},{"_65":162,"_71":116,"_67":163,"_69":164,"_91":157},{"_65":120,"_71":116,"_67":121,"_69":122,"_91":114},{"_65":167,"_71":116,"_67":168,"_69":169,"_91":157},{"_65":483,"_67":484,"_69":485,"_71":72,"_16":486,"_74":487,"_75":488,"_76":379,"_77":380,"_78":489,"_80":-5,"_82":490,"_9":491},"e5aff2c5-8f0a-40ad-ba4f-9c97432ed219","Network Infrastructure Architect","network-infrastructure-architect-at-sambanova-ai-aug-7-2026-c880aa","

About the role

SambaNova is accelerating from rack-scale to global-scale AI compute clusters. We're looking for a Network Architect join us on this exciting new chapter and own this critical layer end to end.

Responsibilities

In this role, you'll be architecting the network fabrics that power hyperscale AI clusters, from RDU-accelerated servers to global-scale deployments, owning the fabric, interconnect, and spine-leaf design decisions that shape how our compute platforms are built. You'll partner closely with hardware engineering, supply chain, and customer-facing teams to translate workload requirements into platform roadmaps, taking a vendor-agnostic approach that avoids lock-in. Your architecture decisions will directly shape the products we ship and how fast the world's largest AI clusters come online.

Required Qualifications

12+ years designing or architecting infrastructure for hyperscale, AI, HPC, or large-scale data center environments
Deep working knowledge of AI cluster networking, including InfiniBand, RoCEv2, Ethernet-based AI fabrics, 400G/800G interconnects, and GPU-to-GPU east-west traffic patterns
Experience with lossless Ethernet designs, including QoS, ECN, PFC, congestion management, buffer tuning, and failure-domain isolation
Strong understanding of spine-leaf architectures and associated technologies (VXLAN, EVPN, BGP, ECMP, underlay/overlay design, network automation)
Familiarity with high-speed optics and cabling for AI clusters, including 400G/800G transceivers, DAC/AOC, fiber topology, and link budgets
Experience translating customer and workload requirements into product requirements, platform roadmaps, and architecture tradeoffs
Vendor-agnostic approach to platform selection and interoperability across switch, NIC, optical, and fabric ecosystems
Ability to partner across hardware engineering, supply chain, operations, and customer-facing teams to bring platforms from concept through deployment

Preferred Qualifications

Experience with AI compute platforms: GPU/accelerator systems, rack-scale compute, NVLink/NVSwitch-class fabrics, PCIe/CXL, DPUs/IPUs
Understanding of rack-level power, thermal, mechanical, and serviceability requirements, and how network choices drive them (NIC selection, cabling density, PCIe lane allocation)
Background in large-scale fleet operations: lifecycle management, reliability, observability, telemetry, field issue resolution

Base Salary Range:

Base Pay Range

$250,000—$330,000 USD

",250000,330000,["D",1786145768000],[492,493],{"_65":85,"_67":86,"_69":87},{"_89":495,"_91":92},{"_89":494,"_91":124},{"_65":125,"_71":126,"_67":127,"_69":128,"_91":129},{"_65":391,"_71":94,"_67":392,"_69":393,"_91":97},{"_65":497,"_67":498,"_69":499,"_71":72,"_16":500,"_74":501,"_75":502,"_76":379,"_77":380,"_78":503,"_80":-5,"_82":504,"_9":505},"ff7a5f80-5d64-4d06-b194-36624a2f8e61","Senior Principal Compiler Engineer","senior-principal-compiler-engineer-at-sambanova-ai-aug-14-2026-b4a54c","

About the team

The compiler team at SambaNova powers the RDU through a unique compilation strategy. By fusing kernels into dataflow graphs that run across the entire accelerator architecture, we avoid sequential kernel launches and eliminate the memory traffic that bottlenecks conventional hardware. This is a key differentiator and unlike any other product on the market.

About the role

As a Senior Principal Compiler Engineer you'll lead critical areas of our compiler including the IR and pass infrastructure, model lowering and partitioning, tensor tiling, memory management, and mapping operations onto the fabric. You'll operate in a tightly coupled environment, influencing hardware design to optimize performance, and bring the technical judgement, cross functional diplomacy, foresight, and low-level programming depth that integrated system design demands. You'll work with senior team members to ensure career growth and engagement, mentor and develop early career talent, actively recruit for the organization, and participate in interviews.

Responsibilities

Lead compiler engineering through ensuring standard methodologies, enterprise product insertion and process evolution.
Work with peers, domain experts, developers, customers, and work across the enterprise seeking optimal solutions.
Develop, integrate, and implement products.
Provide support for proposals in key areas aligned with core team competencies.

Basic Qualifications

Bachelor’s or Master’s Degree in Computer Science, Computer Engineering, or equivalent with 5-10 years of industry experience.

Additional Qualifications

Deep theoretical understanding of compiler fundamentals.
Experience building and deploying software products.
Experience with one or more deep learning frameworks (i.e. TensorFlow, PyTorch) is a plus.
Experience with common compiler development practices and methodologies.
Excitement about high-performance systems engineering and performance debugging.
An appreciation for process and developing cross-disciplinary collaboration.

Preferred Qualifications

Experience with MLIR.
Familiarity with machine learning models and frameworks.
Familiarity with accelerated computing.
Exposure to dataflow architectures.

Base Salary Range:

Base Pay Range

$180,000—$240,000 USD

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Overview

As a Senior Principal Machine Learning Engineer, you will be responsible for designing, developing, and optimizing machine learning models—with a focus on cutting-edge Large Language Models (LLMs)—to run efficiently on SambaNova's specialized hardware architecture, including the RDU. This critical role bridges advanced LLM research and practical deployment, involving the development of model architectures, improving training and inference efficiency, and collaborating on hardware-software co-design with compiler, systems, and hardware teams. The engineer will also act as the ML expert, guiding the integration of LLM solutions into production systems and customer-facing products like SambaStack and SambaCloud, with work spanning the full lifecycle from training and inference to evaluation and data curation.

Qualifications

Bachelor's degree in Computer Science, Electrical Engineering, or related field; advanced degree (MS or PhD) strongly preferred
8+ years of industry experience in machine learning engineering, with a demonstrated record of technical leadership on large-scale or novel ML systems
Deep expertise in LLM training, fine-tuning, inference optimization, and evaluation at scale
Strong background in ML algorithms, deep learning architectures, and modern training methodologies, with the ability to critically evaluate and advance the state of the art
Demonstrated ability to lead and align cross-functional technical efforts, mentor senior engineers, and influence organizational direction without direct management authority
Track record of independently scoping and delivering high-complexity, high-ambiguity technical projects

Key responsibilities

Define and drive technical strategy for ML model development, training pipelines, and inference systems on SambaNova's RDU and broader hardware ecosystem
Lead hardware-software co-design efforts in close collaboration with compiler, systems, and hardware teams—shaping architectural decisions that unlock performance at scale
Identify, evaluate, and champion state-of-the-art ML techniques (e.g., speculative decoding, reinforcement learning, mixture-of-experts, long-context modeling) for adoption and adaptation on reconfigurable dataflow architectures
Serve as the senior technical voice in critical design reviews, architectural decisions, and cross-functional planning—providing guidance that influences product and engineering roadmaps
Mentor and develop principal and senior ML engineers, elevating the technical capabilities of the organization through active collaboration, design feedback, and knowledge transfer
Partner with product and engineering leadership to translate complex ML capabilities into scalable, customer-facing solutions in SambaStack and SambaCloud
Drive resolution of the most complex, ambiguous technical challenges—including those that span organizational boundaries or require novel approaches not yet established in the field

Base Salary Range:

Base Pay Range

$220,000—$300,000 USD

",220000,300000,["D",1786894898000],[524,525,526,527],{"_65":85,"_67":86,"_69":87},{"_89":534,"_91":124},{"_89":530,"_91":124},{"_89":529,"_91":92},{"_89":528,"_91":124},{"_65":233,"_71":126,"_67":234,"_69":235,"_91":129},{"_65":391,"_71":94,"_67":392,"_69":393,"_91":97},{"_65":531,"_71":126,"_67":532,"_69":533,"_91":129},"2e7ba426-631f-44c4-aebd-bceec96038dc","Machine Learning","machine-learning",{"_65":189,"_71":126,"_67":190,"_69":191,"_91":129},{"_65":536,"_67":537,"_69":538,"_71":72,"_16":539,"_74":-5,"_75":-5,"_76":-5,"_77":-5,"_78":540,"_80":-5,"_82":541,"_9":542},"80d9117c-8343-4fc9-879c-16a109f2668b","Principal AI Systems Performance Engineer","principal-ai-systems-performance-engineer-at-sambanova-ai-aug-16-2026-b22107","

About the role

We are seeking a talented and driven ML performance engineer to optimize and scale state-of-the-art foundation models on SambaNova's reconfigurable dataflow platform. You'll work hands-on with some of the most advanced models in the world — such as DeepSeek R1, GPT OSS, and other frontier architectures — to push the limits of throughput, latency, and efficiency. In this role, you'll bridge the gap between deep learning and systems performance, collaborating across compiler, runtime, and hardware layers to deliver world-record performance for large-scale AI inference.

Responsibilities

Bring up and optimize cutting-edge foundation models (e.g., DeepSeek, Llama, Qwen, and others) on the SambaNova platform through the SambaNova software stack.
Profile and enhance model performance across compiler, runtime, and hardware layers to achieve SOTA throughput and latency.
Collaborate with machine learning, compiler, runtime, and hardware teams to deliver co-designed, high-performance AI applications.
Integrate the latest advances in model architecture, quantization, scheduling, and memory optimization from both academia and industry.
Develop robust, scalable, and efficient end-to-end inference solutions aligned with customer needs.
Identify performance bottlenecks and propose dataflow or scheduling optimizations for both single-node and distributed systems.

Basic Qualifications

Bachelor's or higher degree in computer science, electrical engineering, or a related field (e.g., applied mathematics, physics, or statistics).
3+ years of experience in one or more of the following areas:
Deep learning model development and performance optimization
Compiler, runtime, or kernel-level optimization
Software–hardware co-design or systems performance tuning
Proficiency in Python or C++, with strong foundations in algorithms, data structures, and numerical computing.
Experience with at least one major ML framework — PyTorch, TensorFlow, or JAX.
Demonstrated ability to analyze and optimize performance in real-world ML pipelines.

Preferred Qualifications

Hands-on experience with LLM or multimodal model training and inference.
Background in large-scale distributed training, continuous batching, and high-throughput inference systems.
Familiarity with quantization, graph optimization, kernel fusion, and model partitioning.
Experience with frameworks such as DeepSpeed, Megatron, vLLM, or TensorRT.
Strong GPU programming skills (CUDA, Triton, or OpenCL); experience with cuDNN, cuBLAS, or similar libraries is a plus.
Knowledge of memory hierarchy optimization, caching, and scheduling for large-scale model execution.
Publication record or open-source contributions in ML systems or performance optimization is a plus.

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About the role

As an Architect on the Inference Systems Performance team, you'll own the discipline of end-to-end performance for large-scale LLM inference at SambaNova, from how a request moves through tokenization, prefill, decode, and the fabric between them, to how an entire deployment is sized against customer SLOs. Inference-systems performance is a nascent field; the results of design choices are being discovered daily rather than inherited from a mature craft, and this role exists to bring rigor to that frontier.

The work spans two coupled pillars.

The first is reproducible workload capture and benchmarking -- building faithful, replayable representations of real and increasingly agentic traffic, so that what we measure reflects production rather than an artifact of a naive load script.

The second is performance modeling and simulation - analytic and simulation models that turn measurement into a \"what-if\" capability, letting us reason about configurations and hardware that do not exist yet.

Together these feed both today's serving optimization and the next generation of system planning.

The technical frontier you'll help define is heterogeneous, disaggregated inference - GPU on prefill, the RDU on decode - which explores hard problems across networking, storage, prompt caching, and tail-latency-bound data movement.

You will be the go-to person for inference-systems performance across SambaNova, and a resource the entire organization relies on to answer \"how fast can this go, and what will it take.\"

Responsibilities

Define and drive the technical strategy for inference-systems performance including workload capture, benchmarking, modeling, and simulation, while developing and architecture that enables many potential futures
Build the workload-capture and agentic-benchmarking capability - capture representative production traffic and enforce the discipline of interrogating results, spotting artificial contention or misleadingly high cache-hit rates that never occur in real use
Own the performance-modeling and simulation practice - models that predict how a configuration change moves the output, informing capacity planning against customer SLOs and next-generation system and hardware planning
Attack the end-to-end profiling gap - drive tooling that produces accurate, actionable profiles of a distributed inference pipeline so bottlenecks can be localized across host, accelerator, and fabric
Serve as the senior technical voice across model-optimization, systems, hardware, and product, tying together multiple engineering activities and teams, and weighing trade-offs of reliability, scalability, operational cost, and ease of adoption
Act as a resource for the entire organization including representing SambaNova's performance story to customers and partners
Mentor and multiply by raising the capability of principal and senior engineers, building the systems, tools, and patterns that make everyone more productive
Drive the resolution of the most ambiguous, novel challenges that span organizational boundaries or have no established answer in the field yet

Required qualifications

12+ years of experience in performance engineering, with a demonstrated record of technical leadership on large-scale, complex systems
Deep expertise in end-to-end performance analysis of distributed systems with many moving parts and the ability to localize bottlenecks that others cannot
Proven command of realistic workload generation and simulation and of performance modeling, including calibrating models against real, variable workloads
Demonstrated ability to enter an unfamiliar domain and apply core performance methods with transferable discipline expertise
Ability to lead cross-functional efforts, mentor senior engineers, and influence organizational direction
Experience representing an organizations credibly to customers and partners
Track record of independently scoping and delivering high-complexity, high-ambiguity work with significant impact on products or roadmap

Preferred qualifications

Direct experience with LLM inference serving - continuous batching, prompt/KV caching, prefill/decode disaggregation, tail-latency SLOs
Familiarity with inference simulation frameworks or agentic benchmarking efforts
A public technical voice - talks, writing, or community presence on systems performance

Base Salary Range:

Base Pay Range

$245,000—$325,000 USD

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