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Product Manager, Physical AI Agents

Job in Denver, Denver County, Colorado, 80285, USA
Listing for: dexmate
Full Time position
Listed on 2026-07-21
Job specializations:
  • Software Development
    AI Engineer (Applied/Software)
Salary/Wage Range or Industry Benchmark: 120000 - 180000 USD Yearly USD 120000.00 180000.00 YEAR
Job Description & How to Apply Below

About Dexmate

Dexmate is building the foundation for physical AI — combining a new generation of robots with a universal Physical AI OS, making robots as easy to build and deploy as software.

Today, robotics is fragmented, slow, and closed: most builders are forced to reinvent the same stack again and again, and most ideas never make it past the prototype stage. We exist to change that. Our mission is to democratize robotics by lowering the barrier to entry, delivering a plug‑and‑play platform for developers, researchers, and enterprises, and cultivating an open ecosystem that accelerates the evolution of physical AI.

If you want to help shape the next layer of human capability — and believe the future of robotics should be built together, not in isolation — we'd love to build it with you.

About the role

As Product Manager for Physical AI Agents, you'll be at the forefront of defining what general‑purpose robotic agents become next. The form factor of physical AI is still unwritten — what an agent can do, how developers build with it, what "good behavior" means in the real world, and where the line between model and product sits are all open questions.

You will answer them, in product.

You'll set clear priorities and drive improvements across the agent stack — model capabilities, developer surfaces, evaluation systems, and the workflows that turn a robot into something a customer relies on. You'll work shoulder‑to‑shoulder with research, engineering, hardware, and our earliest users, balancing what's possible today against what we want to be possible six months from now.

This role is for a technically deep, opinionated PM who thrives in ambiguity, has strong instincts about where AI agents are going, and wants to define a category rather than manage a backlog.

What you’ll do
  • 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.

What we’re looking for
  • 5+ years of product management experience on technically deep products — AI/ML, agents, developer platforms, robotics, autonomy, or similar. Track record of taking 0 to 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…

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