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MiddleOfficeLondon

Robotics Learning Engineer

H
Humanoid
Уровень
Middle
Формат
Office
О роли

Описание вакансии

About the company

  • Humanoid is building the world's most capable, commercially-scalable, and safe humanoid robots.
  • HMND-01 Alpha is their humanoid platform now running in real industrial pilots.

Responsibilities

  • Post-train manipulation policies via behaviour cloning and RL; own the full loop from data to deployment.
  • Come up with data preprocessing strategies to improve the quality of collected data.
  • Work with the simulation team to set up RL training using digital twin, and then iterate on reward and simulation quality to ensure successful transfer to the real world.
  • Partner with the data collection organization to drive data collection activities for a specific capability: specify what good data looks like, ensure diversity and coverage, and iterate on instructions.
  • Expand observation and action spaces with new components required to support novel capabilities, and work with the Teleoperations team to expose these components to robot operators.
  • Partner with Teleoperations and Controls teams to improve motion smoothness and teleoperation experience.
  • Interface with hardware design team to ensure that manipulation team findings regarding the current generation of hardware are reflected in future designs.

Requirements

  • 3+ years working on robots (industry or research) with shipped artifacts to show for it.
  • A good understanding of modern teleoperation and low-level control stack.
  • Experience with neural network post-training.
  • Familiarity with deep learning infrastructure: streaming datasets, checkpointing & state management, distributed training, PyTorch or JAX.
  • Ability to profile & debug numerics and write maintainable research code.
  • Good familiarity with modern software engineering practices.
  • Ability to document experiments clearly and communicate trade-offs crisply.

Nice to have

  • Experience training VLA models for manipulation (autoregressive, diffusion or flow-matching based).
  • Familiarity with OpenVLA, Physical Intelligence (π) models, or similar open VLA frameworks.
  • Experience applying RL to robotics problems.
  • Publications at top-tier robotics or deep learning conferences or equivalent open-source contributions.

Conditions

  • 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.
  • Real ownership - direct access to founding leadership, meaningful input on product direction.
Стек и навыки

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