Strategic Product Manager, Physical AI & Robotics

Genesis; San Carlos (CA); On-site; USD 140,000 - 190,000; Full time

Job summary

Genesis is seeking a product leader to own the roadmap for our core Physical AI capabilities, translating research breakthroughs into deployable, customer-facing products. You will work at the intersection of research, robotics engineering, and field deployment, turning complex tradeoffs into clear decisions and defining readiness for customer sites.

This role requires tight collaboration with distributed teams and a bias toward field results over abstract theory.

Qualifications

• Track record of shipping technical products with complex tradeoffs.
• Ability to read policy evaluations and discuss sim-to-real gaps without coding.
• Bias toward field deployment over whiteboard exercises.
• Excellent written specs and decision logs.
• Comfort working with incomplete information in fast-moving pre-paradigm field.
• Bonus: experience in robotics, autonomy or related domains.

Responsibilities

• Own the product roadmap for core Physical AI capabilities and translate research breakthroughs into deployable, customer-facing products.
• Bridge research, robotics engineering, and field deployment to resolve tradeoffs.
• Define readiness criteria for customer-site deployments with metrics and thresholds for each release.
• Drive cross-functional alignment across distributed teams in research, engineering, and field operations.

Skills

Product roadmap ownership

Trade-off decision making

Technical reading

Field-oriented approach

Written communication

Ambiguity tolerance

Robotics/autonomy domain knowledge

Job description

Genesis is seeking a product leader to own the roadmap for our core Physical AI capabilities, translating research breakthroughs into deployable, customer-facing products. You will work at the intersection of research, robotics engineering, and field deployment, turning complex tradeoffs into clear decisions and defining readiness for customer sites.

This role requires tight collaboration with distributed teams and a bias toward field results over abstract theory.