General Motors
Senior ML Infrastructure Engineer- Embodied AI
Remote ML Infrastructure Engineering role with clear candidate location fit.
PostedJul 2, 2026
Eligible countries1 accepted country
Seniority signalSenior
Work settingRemote
Accepted candidate locations
USA
Role overview
Senior ML Infrastructure Engineer- Embodied AI
Requirements and responsibilities
Readable role content extracted into sections for faster review.
What You’ll Do:
- 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.
Details
- 3+ years of experienceworking 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++.
- Interestin 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 TensorFlow.
- 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.
- Proficiencywith containerization and orchestration technologies (e.g., Docker, Kubernetes).
Nice to Have:
- The salary range for this role is $153,200.00 to $234,100.00. 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:
- 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.
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