RL Engineer | Humanoid

hirehire

London, United Kingdom

On-site

Who this role is best for

Strong fit for reinforcement learning engineers who build deep learning systems and transition research into production-ready policies, working on-site in London with a flat team structure focused on real-world robotics deployment.

Best fit for

Candidates with 3+ years of deep learning system development experience and a track record of shipping models or publishing work.
— “3+ years building deep learning systems in industry or research, with shipped models or published work to show for it.”
Individuals who can bridge research and real-world application, particularly in sim-to-real transfer and policy deployment.
— “Find ways to bring policies trained in simulation to the real world (sim-to-real).”
Professionals with strong coding fundamentals and the ability to write maintainable research code in Python and PyTorch or JAX.
— “Strong Python and PyTorch or JAX. You can profile, debug numerics and write maintainable research code.”

Things to consider

On-site commitment in London is required, with five days a week presence.
— “Location: London, UK (King’s Cross) – on-site, five days a week”
The role is senior-level but not management-focused, requiring self-driven problem-solving.
— “senior individual contributor role with no management track”

How to stand out

Demonstrate experience with vision-language-action models and their training pipelines.
— “Train language- and vision-conditioned manipulation policies with RL, in simulation and in the real world.”
Prepare for a LeetCode-style coding round by emphasizing strong coding fundamentals and maintainable code.
— “Strong coding fundamentals. The process includes a LeetCode-style coding round, so come prepared.”

Pace · Fast PacedCollaboration · HighAutonomy · HighDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

trained language- and vision-conditioned manipulation policies with RL
built challenging and diverse suites of manipulation tasks in simulation
set up real-world RL training pipelines

Typical background

3+ years in deep learning systems in industry or researchexperience with LLMs, VLMs, image or video generative models

Skills & requirements

Required

Reinforcement LearningDeep LearningPythonPyTorchJAXCoding FundamentalsProblem-solving

Preferred

Robotics SimulatorsLarge-scale RL InfrastructurePublications In Iclr, Icml, Neurips

Stack & domain

PythonPyTorchJAXLeetcodeLlmsVlmsImage Or Video Generative ModelsOpenvlaPhysical Intelligence (π) ModelsLeadershipCommunicationProactiveProblem-solvingTeamworkReinforcement LearningRoboticsAIDeep Learning

About the role

Original posting from hirehire via Ashby

Role: Reinforcement Learning Engineer – Manipulation
Company: Humanoid https://thehumanoid.ai/
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 (π) 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.

Source: hirehire careers (Ashby)