The Role

As a Staff ProductManageryou will define and drive the product strategy, requirements, and execution priorities for the systems that enable large-scale model training, experimentation, evaluation, and developer productivity across GMs autonomous vehicle platform. This role will own critical product surfaces and workflows that help machine learning engineers, data scientists, and autonomy teams prepare training data, configure experiments,monitorprogress, evaluate outcomes, and improve iteration speed.

Success requires strong technical judgment, deep customer empathy for ML practitioners, and the ability to translate complex training workflow needs into clear product requirements. You should be comfortable partnering closely with engineering, infrastructure, data, finance, and autonomy stakeholders; making principled tradeoffs across velocity, cost, reliability, and model quality; and influencing without direct authority in a highly technical environment.

WhatYoullDo

• Own productroadmapandexecution for AI/ML training workflow capabilities that improve model development speed, training reliability, experiment traceability, and developer productivity.

• Deeply understand the end-to-end ML training lifecycle, including dataselection, dataset preparation, training job configuration, orchestration, monitoring, evaluation, debugging, and deployment handoffs.

• Act as thevoice of ML engineers, data scientists, autonomy developers, and infrastructure users bycreating and running pain point intakeloop,identifyingworkflow friction, productivity bottlenecks, and opportunities to reduce cycle time.Own framework to stack-rank and convert them into prioritized productrequirements.

• Define product requirements for training platforms, developer tools, observability systems, workflow automation, experiment management, and performance reporting.

• Drive a metrics-based approach to product decisions by defining KPIs for developer productivity, training throughput, cost efficiency, experiment success rates, and time-to-insight.

• Prioritize product investmentsby balancing customer impact, engineering complexity, infrastructure cost, model quality impact, and business urgency.

• Fundunglamorousreliability and platform tech debt against competing demand for visible features, and of articulatingthattradeoffto senior leaders in terms of throughput and cost beyond engineering hygiene.

• Collaborate with engineering, program management, design, data platform, compute infrastructure, and finance teams to deliver high-impact capabilities on predictable timelines.

• Use data, user research, workflow analysis, and internal benchmarking to inform roadmap decisions andvalidatewhether shipped capabilities improve developer experience and productivity.

• Communicate product status, tradeoffs, risks, and recommendations clearly to senior leaders, technical stakeholders, and cross-functional partners.

• Mentor other product managers and cross-functional partners through technical product best practices, without direct people-management responsibility.

• Stay current on AI/ML platform trends, developer productivity tooling, model training infrastructure, and competitive approaches to large-scale ML operations.

Your Skills & Abilities (Required Qualifications)

• 8+ years of product management or related technical product experience, including ownership of complex software platforms or developer-facing products.

• Experience building products for AI/ML, data science, developer productivity, infrastructure, platform engineering, or other highly technical users.

• Proven ability to define product vision, strategy, requirements, and success metrics for complex software products from concept through delivery and iteration.

• Strong understanding of the ML lifecycle, including data pipelines, model training, experimentation, evaluation, performance analysis, and production handoffs.

• Strong analytical skills with the ability to use quantitative and qualitative evidence toidentifyworkflow bottlenecks, prioritize investments, and measure product impact.

• Technicalproficiencyin working with complex software systems, distributed workflows, cloud or compute infrastructure, and data-intensive products.You must be able to independently reason for distributed training failures, like checkpoint recovery, GPUutilizationloss, job variance, orchestration failures, and hold areasonably technicaldebate with an ML engineer.

• Excellent written and verbal communication skills, including the ability to explain technical concepts, tradeoffs, and recommendations to both technical and non-technical partners.

• Demonstrated ability topartner with andinfluence senior stakeholders andcross-functional teams without direct reporting authority.

• Comfortoperatingin ambiguous, fast-moving technical environments and making clear tradeoffs across speed, quality, cost, reliability, and user experience.

• High ownership, resilience, and curiosity, witha track recordof turning complex customer and engineering problems into practical product outcomes.

WhatWill Give You a Competitive Edge (Preferred Qualifications)

• Masters or Doctorate degree in computer science, engineering, data science, machine learning, ora relatedtechnical field.

• Experience working directly with machine learning engineers, data scientists, or research teams on training platforms, experimentation systems, model evaluation workflows, orMLOpstools.

• Experience improving developer productivity through workflow automation, observability, self-service tooling, platform simplification, or internal product development.

• Experience with autonomous vehicles, robotics, simulation,perception, planning, or other data-intensive AI systems.

• Experience driving change in large, complex organizations while partnering across product, engineering, infrastructure, finance, and program management teams.