About the job
Join Dexmate - Pioneering Physical AI
At Dexmate, we are at the forefront of developing the platform for physical AI, creating general-purpose humanoid robots and the comprehensive infrastructure that supports them. Our goal is to democratize access to physical AI for developers everywhere, akin to how cloud computing transformed software infrastructure. We are in the early stages of building our developer community, providing a unique opportunity for you to help shape its future.
Your Role
Many developers encounter challenges when transitioning their models from a controlled environment to real-world applications. As the Developer Advocate Engineer, you will bridge this gap.
Your mission will be to assist AI/ML developers in understanding the intricacies of deploying their models on humanoid robots, addressing issues such as latency, sensor noise, sim-to-real transfer, on-device inference, and closed-loop control. You will create sample projects, write tutorials, and develop content that positions Dexmate as the go-to platform for serious AI engineers eager to explore physical AI.
This is fundamentally an engineering role; you will write code regularly, with talks and tutorials stemming from your hands-on experience.
Your Responsibilities
Develop and share sample projects demonstrating how AI/ML engineers can train, fine-tune, and deploy models using the Dexmate platform.
Produce and publish technical tutorials weekly, including step-by-step guides, architectural explanations, and deployment walkthroughs tailored for engineers familiar with ML but new to physical AI.
Manage the SDK documentation for AI/ML workflows, ensuring quickstart guides, API references, and Python SDK samples are updated within 48 hours of any platform changes.
Respond to developer inquiries daily on Discord and GitHub Discussions, guaranteeing all questions are answered within 24 hours.
Create reference integrations with foundational AI model providers and publish architectural guides for implementing their models on Dexmate robots.
Present at AI/ML conferences 3–4 times a year, including NeurIPS, ICLR, ICML, CoRL, among others.
Conduct live demonstrations for developers, partners, and potential enterprise clients.
Identify integration challenges and missing platform capabilities, providing feedback to engineering weekly.
Who You Are
Fluent in Python, with substantial experience in ML engineering, including model training, fine-tuning, and inference optimization.

