Senior Simulation Engineer (Manipulation)

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.

Vision

In a world where artificial intelligence opens up new horizons, our faith in its potential unveils a new outlook where, together, humans and machines build a new future filled with knowledge, inspiration, and incredible discoveries. The development of a functional humanoid robot underpins an era of abundance and well-being where poverty will disappear, and people will be able to choose what they want to do. We believe that providing a universal basic income will eventually be a true evolution of our civilization.

Solution

As the demands on our built environment rise, labour shortages loom. With the world’s workforce increasingly moving away from undesirable tasks, the manufacturing, construction, and logistics industries critical to our daily lives are left exposed. By deploying our general-purpose humanoid robots in environments deemed hazardous or monotonous, we envision a future where human well-being is safeguarded while closing the gaps in critical global labour needs.

In this role, 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.

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:

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.