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.
• You'll be the person who helps AI/ML developers understand what happens when their models leave the data center and run on a humanoid robot: latency constraints, sensor noise, sim-to-real transfer, on-device inference, closed-loop control, etc.

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.

Preferred

• Experience with vision-language-action models or embodied AI research
• Hands-on sim-to-real transfer work
• Isaac Sim, MuJoCo, Drake, or Genesis familiarity
• Existing technical blog, GitHub, or YouTube with real traction
• Open-source ML contributions
• Experience deploying models on edge or embedded hardware