Senior Perception Engineer – Spatial Intelligence (London)

Senior Perception Engineer – Spatial Intelligence

Humanoid is the first AI and robotics company in the UK, creating the world’s most advanced, reliable, commercially scalable, and safe humanoid robots. Our first humanoid robot HMND 01 is a next-gen labour automation unit, providing highly efficient services across various use cases, starting with industrial applications. 

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.

What You’ll Do

Architect the spatial intelligence layer that unifies 3D geometry with semantic scene understanding to ensure safe and efficient navigation and locomotion in complex environments
Develop advanced 3D spatial reasoning capabilities that provide our robots with long-term persistent spatial memory and a deep contextual awareness of their surroundings.
Own the entire perception lifecycle by training large-scale multimodal networks and deploying and integerating them for ultra-low latency performance on edge hardware
Bridge the gap between conceptual research and physical reality by implementing state-of-the-art 3D representations that track and interpret the environment state
Collaborate with cross-functional teams to bridge the gap between large-scale model training and reliable robot integration.
Stay ahead of the field, rapidly evaluate new model architectures, benchmarks, and datasets to guide our embodied AI roadmap

We’re Looking For

Exceptional software engineering skills with a proven track record of shipping complex ML systems into production.
Strong background in 3D scene understanding and the design of efficient spatial data structures.
Deep experience in machine learning for vision or embodied AI, ideally with large models (VLMs, transformers, or multi-modal architectures).
Proficiency in PyTorch and hands-on experience building, fine-tuning, and deploying large-scale ML systems.
High-impact individual contributor capable of taking projects from conceptual research to high-performance robot code.
Comfortable working in a fast-moving, research-driven environment with evolving models, data, and tools.

What We Offer

Meaningful time off to rest and recharge: 23 days of annual leave (accrued), separate sick leave, and paid bank holidays and 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.
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.

How to Apply

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Senior Simulation Engineer (Manipulation)

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

As a Senior Simulation Engineer at Humanoid, you will build and maintain simulation environments for dexterous manipulation tasks across industrial, service, and home domains. This is primarily a simulation and reinforcement learning-focused role, so we are looking for experience creating realistic physics-based environments and training RL policies, 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

Design and implement gym environments for manipulation tasks spanning industrial, service, and home settings, defining appropriate observation spaces, action spaces, and reward functions.
Analyze and address reward hacking — identify cases where learned policies exploit reward misspecification and iterate on reward design to produce robust behaviors.
Analyze and reduce the sim-to-real gap by tuning physics parameters, improving asset fidelity, and validating simulation behavior against real-world data.
Work with third-party vendors to procure, validate, and integrate high-quality 3D assets (objects, fixtures, environments) suitable for physics-based simulation.
Ensure correct physics setup — contact dynamics, friction, mass properties, joint limits — so that trained policies transfer reliably to hardware.
Identify and reduce simulation bottlenecks to maximize training throughput and environment step rates.
Improve our simulation-based evaluation and reinforcement learning infrastructure to support rapid iteration and scaling.

What We’re Looking For

3+ years building simulation environments or game-engine-based interactive systems (industry or research) with shipped products, published results, or equivalent artifacts to show for it.
Deep hands-on experience with at least one physics simulator (Isaac Sim/IsaacLab, MuJoCo, PyBullet, Drake) or equivalent game engine experience (Unreal, Unity) with a focus on physically accurate interactions.
Strong practical experience running large-scale parallel simulation on GPU clusters and good familiarity with modern GPU-accelerated simulation infrastructure.
Strong Python; you can profile bottlenecks, debug physics issues, 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 manipulation-specific experience (grasping, contact-rich tasks, deformable objects).
Experience designing reward functions and training RL policies in simulated environments; solid understanding of common failure modes (reward hacking, distribution shift, sim-to-real gap).
Experience with NVIDIA Isaac Sim & IsaacLab specifically.
Experience with domain randomization, system identification, or other sim-to-real transfer techniques.
Publications at top-tier robotics or RL conferences or equivalent open-source contributions.
Familiarity with robocasa, robosuite or similar open-source manipulation simulation 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.

How to Apply

Does this role sound like the perfect fit for you?
Fill in the form and include links or files that showcase the best of what you’ve built and achieved.

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Reinforcement Learning Engineer_Locomanipulation

About the Role

We are looking for a Senior or Staff Reinforcement Learning Engineer to develop learning-based control policies for humanoid robots.

You will design and train reinforcement learning policies that enable dynamic locomotion and loco-manipulation behaviors on real robots. Your work will focus on building scalable training pipelines, designing reward functions and environments, and improving sim-to-real transfer for reliable deployment on hardware.

You will work closely with control and robotics engineers to integrate learned policies into the robot control stack, ensuring stable and robust behavior in real-world conditions.

Development will involve continuous iteration between large-scale simulation and hardware experiments.

The problems you will work on include dynamic locomotion, balance recovery, contact-rich manipulation, and multi-behavior policy learning.

What You’ll Do

MS or PhD in Robotics, Machine Learning, Computer Science, or related field.
Strong experience with reinforcement learning (e.g., PPO, SAC, offline RL).
Experience applying RL to robotics or physical systems.
Experience deploying learned policies on real robotic systems.
Experience with physics-based simulation environments (e.g., Isaac Lab, MuJoCo).
Strong programming skills in Python and/or C++.

We’re Looking For:

Design and train reinforcement learning policies for humanoid robot control.
Build scalable simulation and training pipelines (e.g., Isaac Lab, MuJoCo).
Design reward functions, observation spaces, and curricula for complex behaviors.
Improve robustness and sim-to-real transfer of learned policies.
Deploy and evaluate policies on real robotic systems.
Integrate policies into the control stack.

Nice to have

Experience with RL for locomotion or legged robots.
Experience with sim-to-real transfer.
Familiarity with robot dynamics, control, or whole-body control.

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.

How to Apply

Does this role sound like the perfect fit for you?
Fill in the form and include links or files that showcase the best of what you’ve built and achieved.

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Manipulation Capabilities Engineer

About the Role

In this role, you will work on teaching our robots to manipulate the world around them. This is a role at the intersection of applied deep learning and robotics, and to be set up for success you need both experience of working with real robot hardware (e.g. identifying issues in control or teleop), and applied deep learning (you don’t have to be an expert on cutting edge neural network techniques, but you should be perfectly capable of curating data, fine-tuning a policy on that data and hypothesising potential mitigations when something doesn’t work).

What You’ll Do

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.

We’re Looking For

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.

What We Offer

Competitive salary plus participation in our Stock Option Plan
Paid vacation with adjustments based on your location to comply with local labor laws
Travel opportunities to our Vancouver and Boston offices
Office perks: free breakfasts, lunches, snacks, and regular team events
Freedom to influence the product and own key initiatives
Collaboration with top‑tier engineers, researchers, and product experts in AI and robotics
Startup culture prioritising speed, transparency, and minimal bureaucracy

How to Apply

Does this role sound like the perfect fit for you?
Fill in the form and include links or files that showcase the best of what you’ve built and achieved.

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Whole-Body Control & Safe Reinforcement Learning Engineer

About the Role

We are looking for a Senior or Staff Robot Control Engineer to develop whole-body control systems for bipedal robots.

You will work on a control stack combining whole-body and reinforcement learning, enabling robust loco-manipulation behaviors on real robots. A central focus of the role is ensuring that learning-based policies operate safely within structured control architectures, maintaining stability, predictability, and reliable deployment on hardware.

You will design and deploy control algorithms that run directly on physical robots, iterating between simulation and hardware experiments. You will also help shape the architecture of the robot control stack, defining how learning-based policies integrate with whole-body control and safety mechanisms.

The problems you will work on include dynamic locomotion, contact-rich manipulation, balance recovery, and safe integration of learning-based behaviors.

You will collaborate closely with robotics engineers across Boston, London, and Vancouver working on robot design, locomotion, manipulation, and robot deployment.

What You’ll Do

Design safe whole-body control architectures for bipedal robots.
Develop controllers enabling stable locomotion and loco-manipulation.
Integrate reinforcement learning policies within the control stack, ensuring safe operation and reliable hardware behavior.
Define interfaces between learned policies and structured controllers, enabling supervision and fallback mechanisms.
Validate controllers in simulation and on physical robots.
Contribute to a robust, modular real-time control codebase.

We’re Looking For:

MS or PhD in Robotics, Control, Computer Science, or a related field.
Experience developing control systems for complex robots, ideally legged or humanoid platforms.
Experience with whole-body control or physics-based robot control architectures.
Experience implementing reinforcement learning for robotic systems.
Strong understanding of robot dynamics, kinematics, and multi-contact interaction.
Strong C++ development skills for real-time robotic systems.

Nice to have

Experience with bipedal locomotion or loco-manipulation.
Experience with safe RL approaches (e.g., constrained RL, safety filters, supervised policies).
Background in trajectory optimization, motion generation, or MPC.
Experience with real-time robotics middleware (e.g., ROS2).

What We Offer

Comprehensive health coverage for US‑based employees, including fully paid medical, dental, and vision insurance, with virtual care and employee assistance resources.
Meaningful time off to rest and recharge: 23 days of PTO (accrued), separate sick leave, and paid company holidays.
401(k) retirement plan with employer match.
Equity included–we believe builders should share in what they build.
Free daily catered lunch, snacks, and drinks in‑office.
Collaboration with top‑tier engineers, researchers, and product experts in AI and robotics.
Freedom to influence the product and own key initiatives.

For this role in Massachusetts, the expected base salary range is $200K–$350K USD per year; your placement in that range depends on how your experience maps to our internal leveling.

How to Apply

Does this role sound like the perfect fit for you?
Fill in the form and include links or files that showcase the best of what you’ve built and achieved.

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Reinforcement Learning (RL) Engineer, Manipulation

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.

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..

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.

How to Apply

Does this role sound like the perfect fit for you?
Fill in the form and include links or files that showcase the best of what you’ve built and achieved.

Apply now

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Deep Learning Engineer

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

In this role, 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.

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 salary plus participation in our Stock Option Plan
Paid vacation with adjustments based on your location to comply with local labor laws
Travel opportunities to our Vancouver and Boston offices
Office perks: free breakfasts, lunches, snacks, and regular team events
Freedom to influence the product and own key initiatives
Collaboration with top‑tier engineers, researchers, and product experts in AI and robotics
Startup culture prioritising speed, transparency, and minimal bureaucracy

How to Apply

Does this role sound like the perfect fit for you?
Fill in the form and include links or files that showcase the best of what you’ve built and achieved.

Apply now

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Senior ML Engineer (VLA & Navigation)

`Senior ML Engineer (VLA & Navigation)`

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.

What You’ll Do

Develop next-generation spatial understanding systems for robot locomotion and manipulation, integrating perception and high-level reasoning.
Work on open-ended navigation powered by Vision-Language-Action (VLA) models — enabling robots to understand context, predict intent, and act in complex, dynamic environments.
Design and scale auto-labeling and large-scale data pipelines to train and evaluate multimodal models for navigation and interaction.
Develop and implement scene understanding and 3D reconstruction methods that give robots persistent spatial memory and geometric awareness.
Collaborate with cross-functional research and engineering teams to bring large vision-language models into real-world robotic systems.
Stay ahead of the field — rapidly evaluate new model architectures, benchmarks, and datasets to guide our embodied AI roadmap

We’re Looking For

Deep experience in machine learning for vision or embodied AI, ideally with large models (VLMs, VLAs, transformers, diffusion, or multi-modal architectures).
Strong background in scene understanding, spatial reasoning, or 3D reconstruction from visual data.
Proficiency in PyTorch and hands-on experience building, fine-tuning, and deploying large-scale ML systems.
Strong experimental and research skills — capable of taking projects from concept to model training, evaluation, and robot integration.
Comfortable working in a fast-moving, research-driven environment with evolving models, data, and tools.

What We Offer

Meaningful time off to rest and recharge: 23 days of annual leave (accrued), separate sick leave, and paid bank holidays and 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.
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.

How to Apply

Does this role sound like the perfect fit for you?
Fill in the form and include links or files that showcase the best of what you’ve built and achieved.

Apply now

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