- 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
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.
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.
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.
- Experience with vision-language-action models or embodied AI research
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