Staff ML Infrastructure Engineer - Embodied AI Scaling Foundations

Description

At General Motors, our product teams are redefining mobility. Through a human-centered design process, we create vehicles and experiences that are designed not just to be seen, but to be felt. We’re turning today’s impossible into tomorrow’s standard —from breakthrough hardware and battery systems to intuitive design, intelligent software, and next-generation safety and entertainment features.   

Every day, our products move millions of people as we aim to make driving safer, smarter, and more connected, shaping the future of transportation on a global scale. 

Role:

Are you passionate about accelerating the future of autonomous driving? Join the Embodied AI team at General Motors. Our team is developing and deploying machine learning solutions that support safe and reliable autonomous vehicle behavior across real-world scenarios.

As a Staff ML Infra Engineer, you will drive the development of core systems that enable rapid dataset generation, training, evaluation, and iteration of our most advanced Autonomous Driving models. From enabling large foundational driving models to distilling multi-stage production deployed models, your goal will be to dramatically accelerate the machine learning development cycle from one modeling hypothesis to next. 

You will deliver model training pipelines that are performant, easy to use, and exceptionally reliable. Your success will be measured by the velocity and impact of the ML models that rely on the scalable, intuitive, and high‑performance training platforms you help create. 

What you'll do:

Lead the design, implementation, and deployment of scalable platforms and tools that drive machine learning model training and evaluation workflows across GM. 

Own complex technical projects end-to-end, making key architectural decisions and technical trade-offs. You will be a core contributor to team planning, design reviews, and code quality. 

Take a holistic view of projects, considering their impact across multiple teams, and across a longer timeline. 

Proactively drive technical prioritization. Collaborate closely with partner teams to ensure maximum benefit from the systems we build. 

Help shape our team through technical interviewing with high, well-calibrated standards, and play an essential role in recruiting.  

Mentor and onboard junior engineers and interns, helping them grow their careers. 

What you'll bring:

5+ years of experience building large-scale distributed systems, applications, or advanced ML systems‑scale distributed systems, applications, or advanced ML systems 

Proven track record of designing robust frameworks with high-quality, durable APIs. 

Deep understanding of machine learning algorithms with hands‑on application 

Expertise in building reliable, high-performance, and cost-efficient systems on modern cloud infrastructure‑performance 

End-to-end experience across the ML development lifecycle, including MLOps practices 

Strong cross functional collaboration skills across teams and organizations 

Exceptional coding skills in Python or C++ 

Strong interest in autonomous driving and its transformative potential 

BS, MS, or PhD in Computer Science, Mathematics, or equivalent practical experience 

Nice to have:

Experience with distributed training methodologies 

Experience scaling ML training across large GPU/CPU clusters or other accelerators 

Familiarity with deep learning frameworks (e.g., PyTorch, TensorFlow) 

Experience with performance profiling and state-of-the-art training optimization techniques, including their impact on model performance ‑of‑the‑art training optimization techniques, including their impact on convergence. 

Experience with advanced build systems (e.g., Bazel, Buck, Blaze, CMake) 

Proficiency with containerization and orchestration technologies (e.g., Docker, Kubernetes) 

Remote/Hybrid: This role is categorized as fully remote or hybrid.

Compensation: The compensation information is a good faith estimate only. It is based on what a successful applicant might be paid in accordance with applicable state laws. The compensation may not be representative for positions located outside of the California Bay Area. 

The salary range for this role is $189,300.00 to $290,700.00. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position. 

Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance. 

Benefits: 

GM offers a variety of health and wellbeing benefit programs. Benefit options include medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation & holidays, tuition assistance programs, employee assistance program, GM vehicle discounts and more. 

Relocation: This job may be eligible for relocation benefits. 

Company Vehicle: Upon successful completion of a motor vehicle report review, you will be eligible to participate in a company vehicle evaluation program, through which you will be assigned a General Motors vehicle to drive and evaluate. Note: program participants are required to purchase/lease a qualifying GM vehicle every four years unless one of a limited number of exceptions applies.

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About GM

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Non-Discrimination and Equal Employment Opportunities (U.S.)

General Motors is committed to being a workplace that is not only free of unlawful discrimination, but one that genuinely fosters inclusion and belonging. We strongly believe that providing an inclusive workplace creates an environment in which our employees can thrive and develop better products for our customers.

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We encourage interested candidates to review the key responsibilities and qualifications for each role and apply for any positions that match their skills and capabilities. Applicants in the recruitment process may be required, where applicable, to successfully complete a role-related assessment(s) and/or a pre-employment screening prior to beginning employment. To learn more, visit How we Hire.

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