Senior Product Manager, Labeling

General Motors • Full-time • United States • 3w ago

The Role

As a Senior Product Manager for Data Labeling, you will define and drive the product vision for GM’s labeling ecosystem—spanning UI tools for human annotation, workflow and efficiency optimization, and strategy for automatic and semi‑automatic labeling solutions. You will partner closely with machine learning teams across AV and ADAS to understand their data and model‑training needs, ensuring labeling quality, throughput, and cost‑efficiency scale with the demands of next‑generation perception and planning systems.

This role requires strong product judgment, the ability to balance quality and velocity for different ML customers, and comfort navigating complex tradeoffs between human and automated labeling approaches. You will work across engineering, operations, research, and safety teams to deliver labeling solutions that accelerate development cycles and improve model performance.

What You’ll Do

Own the long‑term product vision and roadmap for GM’s data labeling ecosystem, covering human‑in‑the‑loop labeling tools, workflow orchestration, and automatic labeling technologies.

Define UI and workflow requirements for labeling tools that maximize annotator efficiency, reduce errors, and ensure consistent quality across large‑scale labeling operations.

Develop a strategy and roadmap for automatic and semi‑automatic labeling, including model‑assisted tools, heuristics, and quality‑gating mechanisms.

Partner with AV and ADAS ML teams to deeply understand their data modalities, annotation requirements, and pain points—translating these into clear product requirements and prioritization frameworks.

Drive efficiency investments for human labelers, including process optimization, intelligent task routing, active‑learning loops, and targeted improvements based on productivity metrics.

Balance cost, throughput, and quality when choosing between human, hybrid, and automated labeling solutions.

Lead cross‑functional efforts across Engineering, Operations, ML Research, Design and Program teams to deliver scalable, reliable, and secure labeling infrastructure.

Establish KPIs that measure labeling quality, annotation velocity, end‑to‑end cycle time, ML impact, and cost efficiency—and use them to inform product decisions.

Act as the voice of labeling customers (ML engineers, data scientists, and safety evaluators), ensuring labeling solutions support their development cycles, evaluation workflows, and model iteration velocity.

Your Skills & Abilities (Required Qualifications)

5+ years of product management experience, ideally in data labeling, ML tooling, annotation platforms, or related areas.

Demonstrated ability to translate the needs of ML customers into actionable product requirements and technical tradeoff frameworks.

Strong product judgment with experience solving complex, constrained problems using structured reasoning and data‑informed decisions.

Experience defining workflow and UI requirements for technical or operations‑heavy tools, with a focus on efficiency, usability, and quality outcomes.

Ability to balance competing factors across annotation quality, throughput, cost efficiency, and ML impact.

Strong written communication skills, with the ability to integrate engineering, safety, privacy, and operational perspectives into clear narrative requirements.

Experience identifying and instrumenting key metrics for labeling efficiency, model‑assisted workflows, and ML training effectiveness.

Ability to work across engineering, operations, and research teams to build alignment on roadmap priorities and deliver cross‑functional outcomes.

What Will Give You a Competitive Edge (Preferred Qualifications)

Experience managing products in robotics, autonomous systems, machine learning, or human‑in‑the‑loop systems.

Background in ML, computer vision, or related technical fields.

Familiarity with annotation platforms, automatic labeling systems, or ML data pipelines.

Hybrid: This role is categorized as hybrid. This means the successful candidate is expected to report to the Warren, MI or Mountain View, CA office 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.

The salary range for this role is $106,600 to $192,700. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position.
Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance.

Benefits: GM offers a variety of health and wellbeing benefit programs. Benefit options include medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation & holidays, tuition assistance programs, employee assistance program, GM vehicle discounts and more.

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