Product Manager, Robotics & Physical AI

Responsibilities

• Own the roadmap for robotics data collection, setting direction and driving execution across engineering, design, operations, quality, and solutions.
• Assess demand for new capture modalities, then define the collection model, hardware, and tooling.
• Work with engineering and design on the operator experience: onboarding, task guidance, in-session feedback, error states, review workflows.
• Operators work one-handed, in motion, wearing a headset, or against a clock.
• Build prototypes and write the specs.
• Translate customer model requirements into collection designs, and evolving research into quality standards for egocentric data: pose accuracy, sync tolerance, occlusion handling, annotation schema, acceptance criteria.
• Spend time with collectors, session leads, and reviewers.
• When data underperforms in training, trace it back to the capture design or the tooling that produced it.
• Represent the product directly to hyperscaler and robotics foundation model teams, and bring their constraints back into the roadmap.
• Optimize for yield per session, rejection rate, cost per usable hour, capture-to-delivery time, and operator retention.
• Champion pilots as the default way to test a change. Small cohort, clear hypothesis, fixed window. Scale what works and stop what does not.

Requirements and Qualifications

• 4+ years in product management, including at least 2 in robotics, physical AI, AR/VR, autonomous systems, or data collection at scale.
• Technical degree, or equivalent experience.
• Past work as a software engineer or researcher is a plus.
• Technical depth to hold a real conversation with a research team.
• Direct experience with one or more of: egocentric or wearable data capture, teleoperation systems, robot data pipelines, multi-sensor collection rigs or similar setups.
• Experience defining product vision and roadmaps in ambiguous, fast-moving environments.
• Hands-on robotics background. Robotics, ML engineer on embodied models, teleoperation engineer, or similar.
• Familiarity with imitation learning, VLA models, or manipulation policy training, and the data those methods need.
• Experience managing distributed human operations, including crowd or contractor workforces.
• You have shipped something physical and understand calibration, sensor sync, and coordinate frames, and how they fail.