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Dexmate

Robot type
Humanoid
Location
FremontCaliforniaUSA
Job type
Software
Posted
Mar 22, 2026
Salary
$100,000–$200,000 a year
Full-time

Developer Advocate Engineer

Job description

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.

Job responsibilities

  • Build and publish sample projects that show AI/ML engineers how to train, fine-tune, and deploy models on the Dexmate platform.
  • Write and publish technical tutorials weekly — step-by-step guides, architecture explainers, and deployment walkthroughs written for engineers who know ML but are new to physical AI.
  • Own the SDK documentation for AI/ML workflows: quickstart guides, API reference, Python SDK samples, kept current within 48 hours of any platform change.
  • Answer developer questions daily in Discord and GitHub Discussions — no question unanswered within 24 hours.
  • Build reference integrations with foundation AI model providers and publish architecture guides for running their models on Dexmate robots.
  • Speak at AI/ML conferences 3–4 times per year — NeurIPS, ICLR, ICML, CoRL, and similar.
  • Run live demos for developers, partners, and enterprise prospects.
  • Surface model integration friction and missing platform capabilities to engineering weekly.

Job requirements

  • You write Python fluently and have real ML engineering experience — model training, fine-tuning, inference optimization, or ML infrastructure. You've shipped models that ran in production.
  • Curious about the physical world. You don't need a robotics background, but you find the question "what happens when this model controls a robot arm" genuinely interesting, not intimidating.
  • You've published technical content that got traction — a GitHub repo people starred, a tutorial people bookmarked, a blog post that circulated in ML communities. Show us.
  • You write code other engineers want to copy. Clean, documented, opinionated about the right way to do things.
  • You can write a clear getting-started guide for a developer who just signed up and give a credible technical talk to a room of ML researchers. Same depth, different registers.
  • 3+ years of ML engineering experience — model development, training infrastructure, inference, or MLOps
  • You publish on a schedule. The failure mode this role avoids is someone who plans great content but never ships it.

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