Product Manager, Physical AI Agents

• Own the roadmap. Define the strategic priorities and feature roadmap for our physical AI agent product, from on-robot policies to the developer SDK to the cloud-side evaluation and deployment surfaces. Decide what we build, what we cut, and what we say no to.
• Translate frontier capabilities into product. Work closely with our research team to turn advances in VLAs, imitation learning, and reinforcement learning, world models, etc. into features developers and enterprises can actually use. Stay ahead of model capabilities, not behind them.
• Deeply understand the people building on us. Spend real time with developers, researchers, and enterprise users. Watch agents fail in the wild. Identify the opportunities where our platform can make building physical AI faster, more reliable, and more powerful.
• Define what "good" means. Build the evaluation frameworks, metrics, and review cadences that make agent quality measurable — success rate, recovery behavior, safety envelopes, time-to-first-skill, intervention rate. Quality you can't measure is quality you won't ship.
• Shape the open ecosystem. Decide what we open source, what we publish, and how we cultivate a community of builders. Help define how our platform boosts physical AI development.
• Drive cross-functional execution. Align research, engineering, hardware, design, and GTM around a coherent product narrative. Make tradeoffs in writing. Defend them. Revisit them when the data says you were wrong.

• 5+ years of product management experience on technically deep products — AI/ML, agents, developer platforms, robotics, autonomy, or similar. Track record of taking 0→1 products from research artifact to something people rely on.
• Strong AI agent sense. You have an opinionated, current view of where agents are going. You can look at a model release, a research paper, or a competitor launch and tell us what it means for our roadmap. You use agentic tools daily and have intuitions about what works and what doesn't.
• Deep technical fluency in the modern AI stack: VLAs, imitation learning, RL, evaluation methodology, data pipelines, harness engineering, RAG, tool use, etc. You don't need to train the model, but you need to reason about it as a peer to our researchers.
• Comfort at the hardware/software boundary. You understand sensors, actuators, latency, failure modes, and the gap between sim and real. Physical AI breaks in ways pure software AI doesn't, and you're energized by that, not scared of it.
• Excellent product judgment under ambiguity. You make crisp decisions with incomplete information, communicate them in concise written documents, and update fast when you're wrong.
• Genuine love for builders. You care about the experience of the developer, researcher, or operator using our platform — and it shows in the product.
• High agency and bias for shipping. You ship v1s quickly and iterate to quality. You don't wait for permission. You don't manage a backlog; you define a category.

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