Chief Scientist

Here at Humanoid, we believe in a future where robots amplify human potential. That’s why we’ve set out on a mission to build the world’s most capable, commercially-scalable, and safe humanoid robots. We’re bringing that mission to life with HMND‑01 - our rapidly developed humanoid platform being deployed in real industrial environments - and we’re growing the team to take it even further.

We believe personal robots will become part of everyday life, changing how people use their time and what they can do independently. Making that future possible is one of the most important challenges in AI and robotics. We're hiring a Chief Scientist to help shape the intelligence behind it.

A useful personal robot must understand homes it has never seen, learn new tasks, handle unfamiliar objects, and work safely alongside people. These are fundamental problems with no established blueprint. You will help define the approaches that turn those capabilities into something people can rely on every day.

Our hardware team brings experience from Boston Dynamics, Sanctuary AI, Apptronik, Agility Robotics, and 1X. This is an opportunity to build on lessons from across the field, working alongside engineers who understand the challenges of developing humanoid robots and bringing AI, hardware, and product together.

Reporting to the CTO and working closely with the VP of AI and CEO, you will set scientific priorities, build an exceptional team, and guide advances from early experiments through to deployment. The role offers the opportunity to lead AI for our biped program and develop capabilities that can be shared across our industrial and bipedal platforms.

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  Define the scientific direction for embodied intelligence, with a particular focus on biped AI, and identify the technical advances that matter most to useful robot capabilities.

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  Build and mentor a team of scientists and engineers specialising in robot learning and deep learning, and help develop the team's technical leaders.

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  Evaluate promising approaches in robot learning, world models, dexterous manipulation, whole-body loco-manipulation, and 3D perception, choosing methods according to evidence and the problem at hand.

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  Explore learning loops that turn real-world robot data into simulation environments, train and evaluate capabilities in simulation, and deploy improvements back to robots, including opportunities for agents to automate experimentation.

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  Work closely with AI, engineering, and product teams, including the biped hardware team in Vancouver, to integrate advances into reliable systems and share capabilities across platforms.

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  Set rigorous evaluations and milestones for generalisation, reliability, data efficiency, and human-robot interaction; adapt priorities as experiments and deployment reveal what works.

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  Share the team's technical work through publications and conferences, and develop collaborations with academic and industry partners.

• A record of significant contributions to AI, deep learning, robot learning, or a closely related field, demonstrated through publications in leading venues, methods adopted by others, or deployed systems.

• Deep expertise in one or more relevant areas, such as robot learning, world models, manipulation, whole-body control, 3D perception, or simulation and learning for physical systems. We welcome adjacent expertise with a compelling connection to embodied intelligence.

• Experience defining scientific roadmaps, selecting promising technical approaches, and leading ambitious programs through uncertainty.

• A track record of recruiting and mentoring outstanding scientists and engineers in academia, industry, or both.

• A commitment to turning scientific advances into useful, reliable robots, including the evaluation, integration, and engineering work required for deployment.

• The ability to collaborate across disciplines, explain complex technical ideas clearly, and make sound decisions with engineering, product, and company leadership

• Experience founding or helping build an AI or robotics company, or translating scientific advances into a successful commercial program.

• Experience working across academic institutions and industrial AI organisations.

• Direct work on humanoid or bipedal robots, generalisation in home environments, or human-robot interaction.

• Experience with real-to-sim-to-real learning loops, agent-assisted experimentation, or building and leading AI teams working closely with hardware.