This list contains only the countries for which job offers have been published in the selected language (e.g., in the French version, only job offers written in French are displayed, and in the English version, only those in English).
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. 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.
Job Responsibility:
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
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
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
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
Experience with advanced build systems (e.g., Bazel, Buck, Blaze, CMake)
Proficiency with containerization and orchestration technologies (e.g., Docker, Kubernetes)