Humanoid
Technology
Internship-Controls
Neural analysis suggests this role is
optimal for Entry candidates.
“Internship - Controls at Humanoid. Skills: Reinforcement Learning, Robotics, Machine Learning, Controls. Design reinforcement learning policies. Train reinforcement learning policies”
What You'll Achieve.
Achieve accurate tracking over time; Achieve smooth tracking over time; Achieve stable tracking over time; Achieve robust tracking over time
Industry & Context.
Troubleshoot issues; Problem-solving mindset
What They're Looking For.
Must Have
Hands-on experience with PyTorch, Training ML models, Experience writing code, Comfortable working with hardware, Comfortable with experiments, Comfortable with debugging, Ability to learn quickly, Ability to operate in fast-paced environment, Problem-solving mindset, Attention to detail, Clear communication, Ability to work closely with team
Nice to Have
Interest in reinforcement learning, Interest in machine learning, Interest in robotics
What You'll Do.
Design reinforcement learning policies
Train reinforcement learning policies
Enable dynamic locomotion behaviors
Enable loco-manipulation behaviors
Build scalable training pipelines
Design reward functions
Improve sim-to-real transfer
Integrate learned policies
Ensure stable behavior
Ensure robust behavior
Track desired end-effector trajectory
Achieve accurate position tracking
Achieve smooth position tracking
Introduce unreachable positions
Introduce control delay
Add orientation tracking
How You'll Work.
Team & Collaboration
Work closely with engineers
Full Job Description
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. THE OPPORTUNITY We’re looking for interns who are curious, hands-on, and excited to work directly with robotic systems. This is an open-ended internship where 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. This is a full-time internship (5 days per week) over the summer (mid June - mid September), based in our London Paddington office, where you’ll contribute to real robotic systems from early on with guidance from experienced engineers. Duration: 12 weeks | Start date: June | Compensation: Competitive pay + we'll keep you fed (seriously, our breakfasts and lunches are good) WHAT YOU MIGHT WORK ON - Design and train reinforcement learning policies for humanoid robot control - Build scalable simulation and training pipelines (e.g., Isaac Lab, MuJoCo) - Design reward functions, observation spaces, and curricula for complex behaviors - Run and analyse existing policies - Identify issues, troubleshoot, and propose creative solutions - Document procedures and findings, helping shape the evolution of our humanoids WHAT WE’RE LOOKING FOR - Hands-on experience with PyTorch and training ML models. - Strong interest in reinforcement learning, machine le
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