PagerDuty is looking for a Machine Learning Engineer who is passionate about collaborating with data scientists, product managers and engineers alike. As part of our team, you will help us accelerate the development and extension of products powered by Gen AI and many other shapes of Machine Learning. You’ll be contributing hands-on to the development of the services and pipelines that enable multiple ML/AI features in our product.

You will have the opportunity to collaborate with multiple organizations, taking input and guidance from your senior stakeholders and helping bring our initiatives to reality. You’ll succeed by showcasing excellent capacity to manage time, demonstrating emotional intelligence as you navigate stakeholder relationships, and by continuously improving your technical skill set.

Key Responsibilities

Build and improve the capabilities that enable and accelerate the production of machine learning (ML) and generative AI (genAI) based solutions
Partner with data scientists, effectively sharing engineering context and collaborating to support larger initiatives
Incorporate the best available techniques and practices to how we ship machine learning capabilities to production
Commit to continuously optimizing our workflows and reducing technical debt

Basic Qualifications

3+ years of experience building, designing, and shipping machine learning solutions to production
Proven software development track record with Python
Demonstrated experience with data modeling, database design, extract transform load (ETL) processes, working with unstructured data, and cloud-based data infrastructure tools
Ability to stand up infrastructure building blocks to enable ML processes like data exploration, model training and deployment)

Preferred Qualifications

Experience working with Product teams, ensuring and driving a timely delivery
Exposure to large language models / Generative AI and understanding of the capabilities and use-cases for that technology
Ability to develop and ship machine learning services using container orchestration systems such as kubernetes

Not sure if you qualify?

Apply anyway! We extend opportunities to a broad array of candidates, including those with diverse workplace experiences and backgrounds. Whether you're new to the corporate world, returning to work after a gap in employment, or simply looking to take the next step in your career path, we are excited to connect with you.

Where we work

PagerDuty currently has offices in Atlanta, Lisbon, London, San Francisco, Santiago, Sydney, Tokyo, and Toronto. We offer a hybrid, flexible workplace, with many Dutonians choosing to work primarily from home. We also provide ample opportunities for in-person and virtual connection, like team offsites and volunteering events.

How we work

Our values are deeply embedded in how we operate and the people we bring on board. You will see our values ingrained in how we support our customers, collaborate with our colleagues, develop our products and foster an inclusive and empathetic work culture.

Champion the Customer | Put users first to design great products and experiences.
Run Together | Build strong teams that amplify our impact on users.
Take the Lead | Disrupt and invent to be the first choice for users.
Ack + Own | Take ownership and action to deliver more efficiently to users.
Bring Your Self | Bring your best self to build empathy and trust with users.

What we offer

One way we ensure our employees are inspired to do their best is through a comprehensive total rewards approach that supports them and their loved ones. As a global organization, our programs are competitive with industry standards and aligned with local laws and regulations.

Your package may include:

Competitive salary and company equity
Comprehensive benefits package from day one
Flexible work arrangements
ESPP (Employee Stock Purchase Program)
Retirement or pension plan
Paid parental leave - up to 22 weeks for pregnant parent, up to 12 weeks for non-pregnant parent (some countries have longer leave standards and we comply with local laws)
Generous paid vacation time
Paid holidays and sick leave
Dutonian Wellness Days - scheduled company-wide paid days off in addition to PTO
HibernationDuty - an annual company paid week off when everyone at PagerDuty, with the exception of a small, coverage crew, is asked to take a much needed break to truly disconnect and recharge
Paid volunteer time off - 20 hours per year
Company-wide hack weeks
Mental wellness programs

About PagerDuty

PagerDuty, Inc. (NYSE:PD) is a global leader in digital operations management. The PagerDuty Operations Cloud revolutionizes how critical work gets done, and powers the agility that drives digital transformation. Customers rely on the PagerDuty Operations Cloud to compress costs, accelerate productivity, win revenue, sustain seamless digital experiences, and earn customer trust. More than half of the Fortune 500 and more than two thirds of the Fortune 100 trust PagerDuty including Cisco, Cox Automotive, DoorDash, Electronic Arts, Genentech, Shopify, Zoom and more.

Led by CEO Jennifer Tejada, two-thirds of the PagerDuty board is classified as non-white, with women making up nearly half of all board members. We strive to build a more equitable world by investing 1% each of company equity, product, and employee volunteer time.

PagerDuty is Great Place to Work-certified™, a Fortune Best Place to Work for Women, and a top rated product on TrustRadius and G2.

Go behind-the-scenes @pagerduty on Instagram.

Additional Information

PagerDuty is committed to creating a diverse environment and is an equal opportunity employer. PagerDuty does not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, parental status, veteran status, or disability status.

PagerDuty is committed to providing reasonable accommodations for qualified individuals with disabilities in our job application process. Should you require accommodation, please email [email protected] and we will work with you to meet your accessibility needs.

Machine Learning Engineer (Intermediate Level)

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