Reinforcement Learning Engineer Locomanipulation (Robotics)

TL;DR

Reinforcement Learning Engineer (Robotics): Developing learning-based control policies for hirify.global robots with an accent on dynamic locomotion and loco-manipulation. Focus on building scalable training pipelines, improving sim-to-real transfer, and deploying robust policies on hardware.

Location: Onsite in London, UK

Company

hirify.global is on a mission to build the world's most capable, commercially-scalable, and safe hirify.global robots, utilizing their HMND-01 Alpha platform.

What you will do

• Design and train reinforcement learning policies for hirify.global robot control.
• Build scalable simulation and training pipelines using Isaac Lab and MuJoCo.
• Design reward functions, observation spaces, and curricula for complex behaviors.
• Improve robustness and sim-to-real transfer of learned policies.
• Deploy and evaluate policies on real robotic systems.
• Integrate learned policies into the overall robot control stack.

Requirements

• MS or PhD in Robotics, Machine Learning, Computer Science, or a related field.
• Strong experience with RL algorithms such as PPO, SAC, or offline RL.
• Proven track record of applying RL to robotics or other physical systems.
• Experience deploying learned policies on real robotic hardware.
• Proficiency with physics-based simulation environments (e.g., Isaac Lab, MuJoCo).
• Strong programming skills in Python and/or C++.

Nice to have

• Experience with RL specifically for locomotion or legged robots.
• Deep expertise in sim-to-real transfer techniques.
• Familiarity with robot dynamics, control, or whole-body control.

Culture & Benefits

• 23 days of annual leave, 15 days of paid sick leave, and paid company holidays.
• Fully funded private healthcare for UK employees including mental health support.
• Equity ownership in the company.
• Pension scheme with a total 8% contribution.
• Free daily breakfast, catered lunch, and snacks provided in-office.