GE Senior ML Infrastructure Engineer - Embodied AI Generalmotors · Remote · US · ML Platform & Ops

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’returning today’sim possible 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 peopleas we aim to make driving safer, smarter, and more connected, shaping the future of transportation on a global scale.

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.

• Design, implement, and deploy scalable platforms and tools supporting machine learning training and evaluation workflows across GM.
• Drive complex technical projects with strong ownership of implementation, code quality, and system reliability.
• Contribute to technical design discussions and architectural decisions while collaborating with senior engineers and technical leads.
• Work closely with partner teams to ensure platforms meet real-world ML development needs and maximize adoption.
• Identify technical improvements and help prioritize platform investments to improve performance, reliability, and developer productivity.
• Contribute to a strong engineering culture through high-quality code reviews, documentation, and operational excellence.
• Support onboarding and mentoring of junior engineers and interns.

• 3+ years of experience working onlarge-scale distributed systems, applications, or ML infrastructure.
• Experience designing robust services or frameworks with durable, well-designed APIs.
• Solid understanding of machine learning workflows and hands-on experience applying ML systems in production environments.
• Experience building reliable, high-performance, and cost-efficient systems on modern cloud infrastructure.
• Practical experience across the ML development lifecycle, including model training, deployment, andMLOpspractices.
• Strong cross-functional collaboration skills across teams and organizations.
• Strong coding skills in Python or C++.
• Interest in autonomous driving and large-scale ML systems.
• BS, MS, or PhD in Computer Science, Mathematics, or equivalent practical experience.

• Experience with distributed training methodologies.
• Experience scaling ML training across large GPU/CPU clusters or specialized accelerators.
• Familiarity with deep learning frameworks such asPyTorchor Tensor Flow.
• Experience with performance profiling and training optimization techniques and their impact on model convergence and performance.
• Experience with advanced build systems such as Bazel, Buck, Blaze, orCMake.
• Proficiency with containerization and orchestration technologies (e.g., Docker, Kubernetes).

This role is categorized as fully remote or hybrid.

The compensation information is a good faith estimate only. It is based on what a successful applicant might be paidin accordance with applicable state laws. The compensation may not be representative for positionslocatedoutside of the California Bay Area.

• The salary range for this role is $ to $. 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 incentivepayprogram 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,…