Staff ML Infrastructure Engineer - Embodied AI Scaling Foundations

General Motors•Washington, DC

5d•$171,700 - $335,300•Hybrid

About The Position

Join the Embodied AI team at General Motors, where we are developing and deploying machine learning solutions to support safe and reliable autonomous vehicle behavior. As a Staff ML Infra Engineer, you will drive the development of core systems for rapid dataset generation, training, evaluation, and iteration of advanced Autonomous Driving models. Your goal will be to dramatically accelerate the machine learning development cycle, delivering performant, easy-to-use, and exceptionally reliable model training pipelines. Your success will be measured by the velocity and impact of ML models relying on the scalable, intuitive, and high-performance training platforms you help create.

Requirements

5+ years of experience building large-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
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
5+ years of professional experience

Nice To Haves

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 convergence.
Experience with advanced build systems (e.g., Bazel, Buck, Blaze, CMake)
Proficiency with containerization and orchestration technologies (e.g., Docker, Kubernetes)

Responsibilities

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.
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.