Role overview
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What we're looking for
- 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.
- 5+ years of experience building large-scale distributed systems, applications, or advanced ML systems‑scale distributed systems, applications, or advanced ML systems
- Proven track record of designing robust frameworks with high-quality, durable APIs‑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‑performance, and cost‑efficient systems on modern cloud infrastructure
- End-to-end experience across the ML development lifecycle, including MLOps practices‑to‑end experience across the ML development lifecycle, including
- Strong cross functional collaboration skills across teams and organizations‑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.
- 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 ‑of‑the‑art training optimization techniques, including their impact on convergence
- Experience with advanced build systems (e.g., Bazel, Buck, Blaze, CMake)
Hybrid: This role is categorized as hybrid. This means the successful candidate is expected to report to the Sunnyvale or Mountain View Technical Center in the Bay Area three times per week, at minimum.
Compensation: The compensation information is a good faith estimate only. It is based on what a successful applicant might be paid in accordance with applicable state laws. The compensation may not be representative for positions located outside of the California Bay Area.
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