Staff Product Manager - ML Training Workflow

General Motors•Sunnyvale, CA

1d•$134,700 - $245,000•Hybrid

About The Position

At General Motors, we empower Product Managers to solve challenging customer and business problems. We seek passionate and innovative team members who can collaborate effectively within product management, program management, design, and engineering teams to discover and deliver impactful solutions. We hold our teams accountable for results and seek leaders who can influence teammates, stakeholders, and executives using data and logic. As a Staff Product Manager you 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 GM’s 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, monitor progress, 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.

Requirements

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 to identify workflow bottlenecks, prioritize investments, and measure product impact.
Technical proficiency in 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, GPU utilization loss, job variance, orchestration failures, and hold a reasonably technical debate 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 to partner with and influence senior stakeholders and cross-functional teams without direct reporting authority.
Comfort operating in ambiguous, fast-moving technical environments and making clear tradeoffs across speed, quality, cost, reliability, and user experience.
High ownership, resilience, and curiosity, with a track record of turning complex customer and engineering problems into practical product outcomes.

Nice To Haves

Master’s or Doctorate degree in computer science, engineering, data science, machine learning, or a related technical field.
Experience working directly with machine learning engineers, data scientists, or research teams on training platforms, experimentation systems, model evaluation workflows, or MLOps tools.
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.

Responsibilities

Own product roadmap and execution 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 data selection, dataset preparation, training job configuration, orchestration, monitoring, evaluation, debugging, and deployment handoffs.
Act as the voice of ML engineers, data scientists, autonomy developers, and infrastructure users by creating and running pain point intake loop, identifying workflow friction, productivity bottlenecks, and opportunities to reduce cycle time. Own framework to stack-rank and convert them into prioritized product requirements.
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 investments by balancing customer impact, engineering complexity, infrastructure cost, model quality impact, and business urgency.
Fund unglamorous reliability and platform tech debt against competing demand for visible features, and of articulating that tradeoff to 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 and validate whether 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.