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Senior Engineering Manager, Machine Learning Engineering

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Labelbox is the leading data-centric AI platform for building intelligent applications. Teams looking to capitalize on the latest advances in generative AI and LLMs use the Labelbox platform to inject these systems with the right degree of human supervision and automation. Whether they are building AI products by using LLMs that require human fine-tuning, or applying AI to reduce the time associated with manually-intensive tasks like data labeling or finding business insights, Labelbox enables teams to do so effectively and quickly.

Current Labelbox customers are transforming industries within insurance, retail, manufacturing/robotics, healthcare, and beyond. Our platform is used by Fortune 500 enterprises including Walmart, Procter & Gamble, Genentech, and Adobe, as well as hundreds of leading AI teams. We are backed by leading investors including SoftBank, Andreessen Horowitz, B Capital, Gradient Ventures (Google's AI-focused fund), Databricks Ventures, Snowpoint Ventures and Kleiner Perkins.

About the Role

As engineering manager for the machine learning engineering team at Labelbox, you will lead and grow a team working on building a scalable platform that uses foundation models for real-world AI applications. You will manage the day to day running of the team, including sprint planning, daily scrums, 1:1s, etc. You will also bring to the table domain knowledge in AI/ML infrastructure and platforms, and related tooling. You will work closely with sister teams across the engineering organization to collaborate on key initiatives. This role expects the team manager to be technically sound with domain knowledge in machine learning engineering and frameworks, and willing to roll up their sleeves and start coding if needed.

Your Day to Day

  • Drive engineering execution for your team through guidance and technical leadership.
  • Collaborate with cross-functional teams to prioritize work items, track progress and deliver on commitments.
  • Prioritize tasks, lead the release, and sprint planning efforts.
  • Recruit and grow a high-performing engineering team.
  • Work with each engineer in the team on a career progression plan. Identify and address skill gaps.
  • Interested and able to be a hands-on contributor during the entire development cycle from conception and design through release.
  • Brainstorm with engineers on ML system designs and technologies.
  • Unblock and guide the team to ensure adherence to commitment deadlines and quality standards.
  • Interface and communicate regularly with senior management and technical architects.
  • Work with the broader engineering organization to ensure that the infrastructure and tools we build are performant, scalable, reliable, and secure.
  • Engage with stakeholders, including customers, to understand their needs, gather requirements, and provide expert advice on AI-driven solutions.
  • Contribute to technical documentation, research publications, blog posts, and presentations at conferences and forums.
  • Stay abreast of industry trends and emerging technologies. Analyze, assess and incorporate relevant technologies coming out of various AI research labs.

About You

  • Bachelor’s degree in computer science or related field. Advanced degree preferred.
  • 8+ years of work experience in a software company in the domain of distributed systems, ML engineering, AI/ML infrastructure or platforms.
  • 3+ years of leadership experience managing one or more highly technical teams.
  • Demonstrated experience in project management and tools such as Jira.
  • Experience with agile software development and exhibited end-to-end ownership of projects.
  • A disciplined approach to software development with focus on testing and quality.
  • Extensive software design and architecture skills in large-scale systems and AI/ML systems design.
  • Familiarity with building applications using Foundation Models.
  • Working knowledge of machine learning algorithms, natural language processing, and deep learning frameworks.
  • Working knowledge of Generative AI landscape, including model fine-tuning, experimentation, metrics for model evaluation, monitoring and quality-control.
  • Familiarity with Retrieval Augmented Generation, AI agents architecture, RLHF, building and/or using ML pipelines for training and inference.
  • Proficiency in programming languages such as Java, Python or TypeScript.
  • Experience working with cloud computing platforms and data processing technologies.
  • Excellent communication and collaboration skills.
  • Thrive in a fast-paced environment with a willingness to dive deep and to get things done.
  • Strong desire and demonstrated ability to keep up with industry trends and research in the AI/ML landscape.

Engineering at Labelbox

We build a comprehensive platform and end-to-end tool suite for AI system development. We believe in providing the best user experience at scale with high quality. Our customers use our platform in production environments, daily, to build and deploy AI systems that have a real positive impact in the world.

We believe in collaborative excellence and shared responsibility with decision making autonomy wherever possible. We strive for a great developer experience with continuous fine tuning. How we work is one of the cornerstones of engineering excellence at Labelbox.
We learn by pushing boundaries, engaging in open debate to come up with creative solutions, then committing to execution. We continuously explore and exploit new technologies, creating new and perfecting existing techniques and solutions. Making customers win is our North Star.

Labelbox strives to ensure pay parity across the organization and discuss compensation transparently.  The expected annual base salary range for United States-based candidates is $220,000 - $240,000. This range is not inclusive of any potential equity packages or additional benefits. Exact compensation varies based on a variety of factors, including skills and competencies, experience, and geographical location.

Excel in a remote-friendly hybrid model.

We are dedicated to achieving excellence and recognize the importance of bringing our talented team together. While we continue to embrace remote work, we have transitioned to a hybrid model with a focus on nurturing collaboration and connection within our dedicated tech hubs in the San Francisco Bay Area, New York City Metro Area, and Wrocław, Poland. We encourage asynchronous communication, autonomy, and ownership of tasks, with the added convenience of hub-based gatherings.

Your Personal Data Privacy: Any personal information you provide Labelbox as a part of your application will be processed in accordance with Labelbox’s Job Applicant Privacy notice.

Any emails from Labelbox team members will originate from a email address. If you encounter anything that raises suspicions during your interactions, we encourage you to exercise caution and suspend or discontinue communications. If you are uncertain about the legitimacy of any communication you have received, please do not hesitate to reach out to us at [email protected] for clarification and verification.


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