RL Engineer | Humanoid

HireHire; Deutschland; On-site; EUR 107,000 - 178,000; Full time

Benefits offered by this job

Stock options

Private healthcare

Pension: 8% total contribution

Free meals in the office

Visa sponsorship and relocation to UK

Job summary

Humanoid in London, UK, is seeking a senior Reinforcement Learning Engineer focused on manipulation. This on-site role involves building and deploying RL-driven policies for real robots, with a research-adjacent team delivering production-quality policies that run on the robot.

You will train language- and vision-conditioned policies, design diverse simulation task suites, collect real-world data via teleoperations, and establish robust RL pipelines with testing/ops.

Qualifications

• 3+ years building deep learning systems in industry or research.
• Proven RL experience solving real problems with deep nets.
• Experience training models on LLMs, VLMs or generative models.

Responsibilities

• Train language- and vision-conditioned manipulation policies with RL, in simulation and in the real world.
• Build challenging and diverse suites of manipulation tasks in simulation.
• Work with Teleoperations to collect trajectories in simulation for behaviour cloning.
• Work with Testing and Operations to set up real-world RL training pipelines.
• Find ways to bring policies trained in simulation to the real world (sim-to-real).

Skills

Deep learning

Reinforcement Learning

Python

PyTorch

JAX

LeetCode

English

Tools

PyTorch

JAX

Job description

Role: Reinforcement Learning Engineer - Manipulation
Company: Humanoid
Location: London, UK (King's Cross) - on-site, five days a week
Job Type: Full-time

About Humanoid

Humanoid is building the world's most capable, commercially scalable and safe humanoid robots. Its platform, HMND-01, is already deployed in real industrial environments, which makes Humanoid the most serious humanoid robotics company in Europe.

• 300+ people, 34+ nationalities, flat structure: your decisions ship directly into real robots.

• Offices in London (HQ, King's Cross), Boston, Vancouver and San Diego.

• Values: Speed, Ambition, Passion, Trust.

About the role

The VLA Core team builds the base vision-language-action model the whole company runs on, covering pre-training, world models and RL. In this role you use reinforcement learning, in simulation and on real hardware, to turn that model into high-performing, robust manipulation policies.

This is a senior individual contributor role with no management track. Robotics experience is not required: very few people have done RL on real robots, so strong RL in any domain is what matters. The team is research-adjacent, but the output is policies that work on the robot. Results are shared as blog posts, not papers.

What you are expected to do

• Train language- and vision-conditioned manipulation policies with RL, in simulation and in the real world.

• Build challenging and diverse suites of manipulation tasks in simulation.

• Work with Teleoperations to collect trajectories in simulation for behaviour cloning.

• Work with Testing and Operations to set up real-world RL training pipelines.

• Find ways to bring policies trained in simulation to the real world (sim-to-real).

What you need to succeed

• 3+ years building deep learning systems in industry or research, with shipped models or published work to show for it.

• You have solved real problems with RL and deep neural networks, in any domain.

• You have trained models yourself (architecture, training and inference) on at least one of: LLMs, VLMs, image or video generative models. Agent building or LLM integration work alone is not enough.

• Strong Python and PyTorch or JAX. You can profile, debug numerics and write maintainable research code.

• Strong coding fundamentals. The process includes a LeetCode-style coding round, so come prepared.

• You are proactive: you can take an open, messy problem and drive it without waiting for a spec. You document experiments clearly and explain trade-offs crisply.

• Working English.

Nice to have

• Experience with robotics simulators (Isaac Sim, MuJoCo, etc.).

• Experience applying RL to robotics.

• Experience building infrastructure for large-scale RL (e.g. Ray).

• Publications at ICLR, ICML, NeurIPS or equivalent open-source contributions.

• Familiarity with OpenVLA, Physical Intelligence (pi) models or similar open VLA frameworks.

Interview process

• Intro call with Igor, hirehire's AI researcher.

• HR screen with Humanoid.

• Coding round (LeetCode-style).

• RL specialization interview.

• Technical execution interview: how you work, collaborate and drive problems.

• Optional chat with a tech leader.

What you get

• Salary: a wide range, set by the level you show in the interviews.

• Equity: stock options with meaningful upside as Humanoid scales.

• 30+ paid days off: 23 days annual leave, all UK bank holidays and a company shutdown between Christmas and New Year.

• Private healthcare: fully funded, including virtual and in-person care.

• Pension: 8% total contribution (5% employee, 3% employer) on full earnings.

• Daily food: free breakfast, catered lunch and snacks in the office.

• Real ownership: direct access to founding leadership, real input on product direction and room to drive key initiatives from day one.

• Visa sponsorship and relocation to London.