At a Glance
- Tasks: Train robots using reinforcement learning to enhance their manipulation skills in real-world scenarios.
- Company: Join Humanoid, a pioneering tech company revolutionising robotics for human potential.
- 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, innovative environment.
- Why this job: Be part of a mission to create advanced humanoid robots that make a real difference.
- Qualifications: 3+ years in deep learning with hands-on experience in RL and strong Python skills.
The predicted salary is between 70000 - 90000 £ 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're hiring a Reinforcement Learning Engineer to join our Autonomy team based in London. In this role you will leverage reinforcement learning in both simulation and physical reality to build highly performant and robust manipulation policies.
What You'll Do
- Train language-vision conditioned manipulation policies via reinforcement learning (RL) in simulation and in the real world.
- Construct challenging and diverse suites of manipulation tasks in simulation.
- Partner with teleoperations to collect trajectories in simulation for behaviour cloning.
- Partner with testing and operations to establish real-world RL training pipelines.
- Experiment with various ways of bringing policies trained in simulation to the real world.
What We're Looking For
- 3+ years building deep-learning systems (industry or research) with shipped models or published artifacts to show for it.
- Hands-on with at least one of: LLMs, VLMs, or image/video generative models — architecture, training, and inference.
- Experience solving real problems using reinforcement learning with deep neural networks in any domain.
- Strong Python + PyTorch/JAX; you can profile, debug numerics, and write maintainable research code.
- You are self-driven, pro-active, communicate efficiently, document experiments clearly and communicate trade-offs crisply.
Nice to have
- Experience with simulators for robotics (Isaac Sim, MuJoCo etc.)
- Experience in RL for robotics.
- Experience building infrastructure for large-scale RL (e.g. using ray).
- Publications at ICLR/ICML/NeurIPS or equivalent open-source contributions.
- Familiarity with OpenVLA, Physical Intelligence (π) models, or similar open VLA frameworks.
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 - Manipulation employer: Thehumanoid
Thehumanoid is an exceptional employer that prioritises employee well-being and professional growth, offering a dynamic work culture in vibrant locations like London, Boston, and Vancouver. With competitive equity, over 30 days of time off, and comprehensive private healthcare, we empower our team to thrive both personally and professionally while leading innovative supply chain solutions.
StudySmarter Expert Advice🤫
We think this is how you could land Reinforcement Learning Engineer - Manipulation
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to reinforcement learning and robotics. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice coding challenges and be ready to discuss your past projects in detail. We want to see how you think and approach problems!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you’re genuinely interested in joining our mission at Humanoid.
We think you need these skills to ace Reinforcement Learning Engineer - Manipulation
Some tips for your application 🫡
Show Your Passion:When writing your application, let your enthusiasm for reinforcement learning and robotics shine through. We want to see that you’re genuinely excited about the role and our mission at Humanoid!
Tailor Your Experience:Make sure to highlight your relevant experience with deep learning systems and reinforcement learning. We’re looking for specific examples of projects or models you've worked on that align with what we do.
Be Clear and Concise:Keep your application clear and to the point. We appreciate well-structured documents that communicate your skills and experiences without unnecessary fluff. Remember, clarity is key!
Apply Through Our Website:Don’t forget to submit your application through our website! It’s the best way for us to receive your details and ensures you’re considered for the role. We can’t wait to hear from you!
How to prepare for a job interview at Thehumanoid
✨Know Your Stuff
Make sure you brush up on reinforcement learning concepts and the specific technologies mentioned in the job description, like Python and PyTorch. Be ready to discuss your past projects and how they relate to building manipulation policies.
✨Showcase Your Experience
Prepare to talk about your hands-on experience with deep-learning systems and any relevant publications or contributions. Highlight specific challenges you've faced and how you overcame them, especially in relation to RL for robotics.
✨Demonstrate Problem-Solving Skills
Be ready to discuss real-world problems you've solved using reinforcement learning. Think of examples where you had to experiment with different approaches and how you brought simulation results into the real world.
✨Ask Insightful Questions
Prepare thoughtful questions about the company's projects and future directions. This shows your genuine interest in their mission and gives you a chance to assess if it's the right fit for you.
