Senior ML Infrastructure Engineer - Embodied AI
At General Motors, our product teams are redefining mobility. Through a human-centered design process, we create vehicles and experiences 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.
About the role
Join the Embodied AI team at General Motors and help accelerate the future of autonomous driving. Our team develops and deploys machine learning solutions that support safe and reliable autonomous vehicle behavior across real-world scenarios. As a Senior ML Infra Engineer, you will work on the core systems enabling rapid dataset generation, training, evaluation, and iteration of our most advanced Autonomous Driving models. Your goal will be to dramatically accelerate the machine learning development cycle from one modeling hypothesis to the next by building performant, easy-to-use, and exceptionally reliable training pipelines.
Responsibilities
- 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 enhance performance, reliability, and developer productivity.
- Foster a strong engineering culture through high-quality code reviews, documentation, and operational excellence.
- Support onboarding and mentoring of junior engineers and interns.
Requirements
- 3+ years of experience working on large-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, and MLOps practices.
- 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.
Nice to Have
- Experience with distributed training methodologies.
- Experience scaling ML training across large GPU/CPU clusters or specialized accelerators.
- Familiarity with deep learning frameworks such as PyTorch or 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, or CMake.
- Proficiency with containerization and orchestration technologies (e.g., Docker, Kubernetes).
Schedule
This role is categorized as fully remote or hybrid.
Pay
The salary range for this role is $153,200 to $234,100. The actual base salary will vary based on factors relevant to the position. An incentive pay program offers payouts based on company performance, job level, and individual performance.
Benefits
- Medical, dental, and vision insurance
- Health Savings Account and Flexible Spending Accounts
- Retirement savings plan
- Sickness and accident benefits
- Life insurance
- Paid vacation and holidays
- Tuition assistance programs
- Employee assistance program
- GM vehicle discounts
- Relocation benefits (if eligible)