At a Glance
- Tasks: Design and train reinforcement learning policies for humanoid robots and improve their real-world performance.
- Company: Join Humanoid, a pioneering tech company on a mission to enhance human potential with robots.
- Benefits: Enjoy 23 days annual leave, private healthcare, equity options, and free meals at the office.
- Other info: Collaborate with top engineers and researchers in a dynamic and supportive environment.
- Why this job: Be at the forefront of robotics innovation and make a tangible impact on future technologies.
- Qualifications: MS or PhD in relevant fields and strong experience in reinforcement learning and robotics.
The predicted salary is between 60000 - 80000 € per year.
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 behaviours 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 controls and robotics engineers to integrate learned policies into the robot control stack, ensuring stable and robust behaviour 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‑behaviour policy learning.
What You’ll Do
- 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 behaviours.
- Improve robustness and sim‑to‑real transfer of learned policies.
- Deploy and evaluate policies on real robotic systems.
- Integrate policies into the control stack.
What We're Looking For
- MS or PhD in Robotics, Machine Learning, Computer Science, or related field.
- Strong experience with reinforcement learning (e.g., PPO, SAC, offline RL).
- Experience applying RL to robotics or physical systems.
- Experience deploying learned policies on real robotic systems.
- Experience with physics‑based simulation environments (e.g., Isaac Lab, MuJoCo).
- Strong programming skills in Python and/or C++.
Nice to have:
- Experience with RL for locomotion or legged robots.
- Experience with sim‑to‑real transfer.
- Familiarity with robot dynamics, control, or whole‑body control.
What We Offer
- Meaningful time off to rest and recharge: 23 days of annual leave (accrued), 15 days of paid sick leave, and paid company holidays.
- Fully funded private healthcare for UK employees, with broad provider access, virtual and in‑person care, and strong mental health and serious illness support.
- Equity included–we believe builders should share in what they build.
- Pension scheme with a total 8% contribution (5% employee, 3% employer) on full earnings.
- Free daily breakfast, catered lunch, and snacks in‑office.
- Collaboration with top‑tier engineers, researchers, and product experts in AI and robotics.
- Freedom to influence the product and own key initiatives.
Reinforcement Learning Engineer - Locomanipulation employer: Groupe-Ebra-1
At Humanoid, we are dedicated to pushing the boundaries of robotics and enhancing human potential through innovative technology. As a Reinforcement Learning Engineer, you will thrive in a collaborative environment that values your contributions, offering meaningful benefits such as generous leave, comprehensive healthcare, and equity sharing. Join us in our mission to create cutting-edge humanoid robots while enjoying a vibrant work culture that fosters professional growth and creativity.
StudySmarter Expert Advice🤫
We think this is how you could land Reinforcement Learning Engineer - Locomanipulation
✨Tip Number 1
Network like a pro! Reach out to people in the robotics and AI fields, especially those who work at Humanoid. A friendly chat can open doors that applications alone can't.
✨Tip Number 2
Show off your skills! If you’ve got projects or research related to reinforcement learning or robotics, make sure to highlight them in conversations. Real-world examples can really impress.
✨Tip Number 3
Prepare for technical interviews by brushing up on your coding skills in Python and C++. Practice common algorithms and reinforcement learning concepts to ace those tricky questions.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining the team.
We think you need these skills to ace Reinforcement Learning Engineer - Locomanipulation
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the role of Reinforcement Learning Engineer. Highlight your experience with reinforcement learning, robotics, and any relevant projects you've worked on. We want to see how your skills align with our mission!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Share your passion for robotics and how you can contribute to our team. Be specific about your experience with sim-to-real transfer and training pipelines, as these are key to what we do at Humanoid.
Showcase Your Projects:If you've worked on any cool projects related to RL or robotics, make sure to mention them! Include links to your GitHub or any publications if applicable. We love seeing practical applications of your skills!
Apply Through Our Website:Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. We can’t wait to see what you bring to the table!
How to prepare for a job interview at Groupe-Ebra-1
✨Know Your Reinforcement Learning Stuff
Make sure you brush up on your reinforcement learning concepts, especially those relevant to robotics. Be ready to discuss algorithms like PPO and SAC, and how you've applied them in real-world scenarios. This will show that you not only understand the theory but can also implement it effectively.
✨Show Off Your Simulation Skills
Familiarise yourself with physics-based simulation environments like Isaac Lab and MuJoCo. Prepare to talk about any projects where you've built scalable training pipelines or improved sim-to-real transfer. Sharing specific examples will demonstrate your hands-on experience and problem-solving abilities.
✨Collaborate Like a Pro
Since you'll be working closely with controls and robotics engineers, highlight your teamwork skills. Think of instances where you've successfully collaborated on projects, especially those involving dynamic locomotion or multi-behaviour policy learning. This will show you're a team player who can integrate learned policies into the control stack.
✨Ask Smart Questions
Prepare thoughtful questions about the company's mission and the HMND-01 Alpha platform. Inquire about their approach to balance recovery and contact-rich manipulation. This not only shows your genuine interest in the role but also helps you gauge if the company aligns with your career goals.
