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 - our rapidly developed humanoid platform being deployed in real industrial environments - and we’re growing the team to take it even further.

About the Role

We're hiring a VLA Pre-training Engineer to join our Autonomy team based in London. In this role you will you will work on all aspects of training capable policies, be it pre-training of a base model on a diverse multi-embodiment corpus of trajectories, fine-tuning a policy to perform a specific task well, curating data collection processes or exploring productive ways to generate and use synthetic data. This is primarily a deep learning-focused role, so we are looking for experience solving real problems using modern neural networks, while experience in robotics isn’t strictly required. However if you don’t have such experience, be prepared that you’d need to familiarize yourself with a new domain quickly.

What You'll Do

• Post-train policies via behaviour cloning and RL; own the full loop from data to deployment.

• Partner with the Data Collection team to drive collecting new data: specify what good data looks like, identify failure modes, ensure diversity and coverage.

• Work closely with external partners to ensure steady supply of high-quality pretraining-scale data.

• Run pre-/mid-/post-training on VLA stack; explore new modalities and architecture changes.

• Build and maintain continuous pipelines: ingest synthetic data and teleop logs, version them, apply weak‑supervision labelling, curate balanced datasets, and auto‑surface fresh failure cases into retraining.

• Work with MLOps & Data Platform teams to scale distributed training and optimize models for real‑time edge inference.

What We're Looking For

• 3+ years building deep‑learning systems (industry or research) with shipped models or published artifacts to show for it.

• Deep hands‑on experience with at least one of: LLMs, VLMs, or image/video generative models — architecture, training, and inference.

• Experience with deep learning infrastructure: streaming datasets, checkpointing & state management, distributed training strategies.

• Strong Python + PyTorch/JAX; you can profile, debug numerics, and write maintainable research code.

• Familiarity with modern software engineering practices.

• You document experiments clearly and communicate trade‑offs crisply.

Nice to have

• Robotics or autonomous driving experience.

• Experience applying RL to LLMs or robotics.

• Experience with VLA (vision-language-action) models.

• Proven productization of deep nets (latency/throughput constraints, telemetry, on‑device optimization).

• Publications at top-tier deep learning conferences or equivalent open‑source contributions.

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

What We Offer

• Competitive equity: stock options with meaningful upside as we scale.

• 30+ paid days off, including 23 days of annual leave, all UK bank holidays, and additional company closure days (including Christmas–New Year shutdown).

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

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

• Free daily breakfast, catered lunch, and snacks in-office.

• Work at the frontier - collaborate daily with world-class engineers, researchers, and product experts building the next generation of AI and humanoid robotics.

• Real ownership - direct access to founding leadership, meaningful input on product direction, and the ability to drive key initiatives from day one.

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 - our rapidly developed humanoid platform being deployed in real industrial environments - 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 behavior 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

• Competitive equity: stock options with meaningful upside as we scale.

• 30+ paid days off, including 23 days of annual leave, all UK bank holidays, and additional company closure days (including Christmas–New Year shutdown).

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

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

• Free daily breakfast, catered lunch, and snacks in-office.

• Work at the frontier - collaborate daily with world-class engineers, researchers, and product experts building the next generation of AI and humanoid robotics.

• Real ownership - direct access to founding leadership, meaningful input on product direction, and the ability to drive key initiatives from day one.

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 - our rapidly developed humanoid platform being deployed in real industrial environments - and we’re growing the team to take it even further.

Our Mission

At Humanoid we strive to create the world's leading, commercially scalable, safe, and advanced humanoid robots that seamlessly integrate into daily life and amplify human capacity.

About the Role

As a Research Engineer on the World Models team, you will build action-conditioned generative models that predict how the world evolves around our robots — future video, proprioception, contacts, and outcomes — from past observations and actions. World models serve four purposes in our stack: a pretrained, physics-aware prior for our VLA policies; an engine for rare data collection, cross-platform transfer, and sim-to-real transfer; a testbed for policy evaluation and testing before hardware; and a future-prediction rollout engine that surfaces what our policies intend to do, for safety and planning. This is a hands-on individual contributor role: you will design architectures, run large training jobs, and validate your models against real fleet data from industrial deployments.

What You'll Do

• Design and train multimodal world models — video, state, action, and language — using diffusion-based and transformer architectures.

• Build action-conditioned video prediction and dynamics models that stay physically consistent over long horizons, including contact-rich manipulation, and serve as pretrained priors for VLA policies.

• Develop learned-simulator evaluation: score candidate policies offline, predict real-world success rates before deployment, and roll out policy futures to expose intended behaviour for safety review and planning.

• Generate synthetic rollouts and counterfactual experience — including rare events, cross-platform transfer, and sim-to-real transfer — to augment policy training, and measure their effect on downstream task performance.

• Establish fidelity metrics and calibration protocols that quantify where the world model can be trusted and where it diverges from reality.

• Build data pipelines that turn fleet telemetry, teleoperation logs, and internet-scale video into training corpora for world models.

• Run scaling and ablation studies on architecture, data mixture, and context length; communicate findings crisply.

• Collaborate with pretraining, RL, and manipulation teams to integrate world models into policy training and evaluation loops.

What We're Looking For

• A track record of training large generative models — video, world, or multimodal — with shipped models or published artifacts to show for it.

• Deep hands-on experience with modern generative architectures: diffusion models, autoregressive transformers, latent-variable models, or video prediction.

• Experience with large-scale distributed training: streaming datasets, checkpointing and state management, debugging numerics and training instabilities.

• Strong Python + PyTorch/JAX; you can profile kernels, optimize data loaders, and write maintainable research code.

• Empirical rigor: you design careful evaluations, run honest baselines, and document experiments clearly.

• Excitement about grounding generative models in physical reality rather than pixels alone.

Nice to have

• Experience with world models for robotics or autonomous driving (e.g., action-conditioned video models, learned simulators, model-based RL).

• Familiarity with robotics simulators (Isaac Sim, MuJoCo) and sim-to-real considerations.

• Experience using world models for policy evaluation or synthetic data generation at scale.

• Publications at top-tier deep learning conferences (NeurIPS, ICML, ICLR, CoRL, CVPR) or equivalent open-source contributions.

• Experience optimizing generative models for fast inference.

What We Offer

• Competitive equity: stock options with meaningful upside as we scale.

• 30+ paid days off, including 23 days of annual leave, all UK bank holidays, and additional company closure days (including Christmas–New Year shutdown).

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

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

• Free daily breakfast, catered lunch, and snacks in-office.

• Work at the frontier - collaborate daily with world-class engineers, researchers, and product experts building the next generation of AI and humanoid robotics.

• Real ownership - direct access to founding leadership, meaningful input on product direction, and the ability to drive key initiatives from day one.