
Humanoid develops general-purpose humanoid robots for industrial and daily tasks. The technology combines AI and robotics to create adaptable, safe robots for hazardous and monotonous environments.…

Humanoid develops general-purpose humanoid robots for industrial and daily tasks. The technology combines AI and robotics to create adaptable, safe robots for hazardous and monotonous environments.…
What they build: Commercially scalable humanoid robots (HMND 01 family) and an AI stack called KinetIQ
Target customers: Enterprise automation for manufacturing, warehouses, logistics, retail and service sectors
Founded: 2024 (UK-based)
Notable funding: Reported Seed round June 2024 — $50.0M
Head: Founder & CEO: Artem Sokolov
Industrial and enterprise automation (manufacturing, warehouses, logistics, retail, 3PL, service/household tasks).
2024
Robotics Engineering
$50.0M
Reported seed round in June 2024 sized at $50.0M.
“Mitch Kapor listed as a lead investor on a company profile”
| Company |
|---|
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 Alpha - our rapidly developed humanoid platform now running in real industrial pilots - and we’re growing the team to take it even further.
About The Role We are looking for a Senior or Staff Reinforcement Learning Engineer to develop learning-based control policies for humanoid robots.
You will design and train reinforcement learning policies that enable dynamic locomotion and loco-manipulation behaviors on real robots. Your work will focus on building scalable training pipelines, designing reward functions and environments, and improving sim-to-real transfer for reliable deployment on hardware.
You will work closely with control and robotics engineers to integrate learned policies into the robot control stack, ensuring stable and robust behavior in real-world conditions.
Development will involve continuous iteration between large-scale simulation and hardware experiments.
The problems you will work on include dynamic locomotion, balance recovery, contact-rich manipulation, and multi-behavior policy learning.
What You’ll Do
What We're Looking For
Nice To Have
What We Offer
Your next opportunity is in here somewhere. Sign up to explore 52,000+ startups and their open roles. No spam. No gamification. Just jobs.
52,000+
Startups
65,000+
Open Roles
1,300+
New This Week