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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. As a Staff ML Engineer, you will build critical infrastructure that powers every machine learning engineer working on our cutting-edge Autonomous Driving models. From foundational models to state-of-the-art optimization, our goal is simple: dramatically accelerate the machine learning development cycle. We are committed to delivering products that are performant, easy to use, and exceptionally reliable. Your success will be measured by the success of our partner teams who rely on our robust systems to build the world's most advanced driverless vehicles.
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. You will 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 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
Proficiency with containerization and orchestration technologies (e.g., Docker, Kubernetes)
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)