Hybrid: This role is categorized as hybrid. The successful candidate is expected to report to GMs office in Mountain View, CA or the office in Warren, MI as needed. Why join us? General Motors pioneers the innovations that move and connect people to what matters. We see a world with zero crashes, zero emissions, and zero congestion. As we move toward this vision, software plays an integral role as it becomes more prominent in our vehicles. 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. The Role As a Staff Product Manager for Logging and Offload, you will define and drive the data logging and offload strategy for GM’s next generation of autonomous vehicles. This role will own the roadmap for how we capture, manage, and prioritize data across a global fleet—what’s logged, when, why, and how it’s offloaded from the vehicle. You’ll lead cross-functional efforts to design scalable onboard and offboard systems that power autonomy development, regulatory compliance, and fleet optimization. Success requires strong technical judgment, principled decision-making, and the ability to build alignment across engineering, infrastructure, and operations teams. You should be comfortable navigating ambiguity, making tough tradeoffs, and thinking both from first principles and at fleet-wide scale. The Role As a Senior Staff Product Manager for Model Training, you will define and drive our ecosystem of technical solutions for large scale model training to drive the advancement of autonomous vehicle solutions. This role will own the roadmap for how we prepare, track, and manage our model training work to maximize productivity and efficiency. This role requires collaboration with data scientists, engineers, and stakeholders, as well as a deep understanding of the ML lifecycle, from data acquisition and training to performance evaluation and deployment. Success requires strong technical judgment, principled decision-making, and the ability to build alignment across engineering, infrastructure, and finance teams. You should be comfortable navigating ambiguity, making tough tradeoffs, and translating business needs into engineering priorities.
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Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed