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Robotics Control Engineer - Reinforcement Learning

Humanoid

London

On-site

GBP 50,000 - 85,000

Full time

4 days ago

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Job summary

A leading robotics company in the UK is searching for a Senior Reinforcement Learning Control Engineer in London. This role involves developing advanced control algorithms for humanoid robots focusing on locomotion and dynamic maneuvers, ensuring safety and efficiency. Candidates should have a strong background in reinforcement learning, control systems, and practical experience deploying control systems on physical robots.

Benefits

High competitive salary

23 working days of vacation per year

Opportunity to work on the latest technologies in AI and Robotics

Dynamic and innovative work environment

Qualifications

3+ years experience in control systems for legged robots.
Strong expertise in RL applied to robotics.
Hands-on experience with hardware-in-the-loop testing.

Responsibilities

Design and implement RL-based control policies for locomotion tasks.
Model and simulate complex dynamics for optimization.
Conduct rigorous testing to validate algorithms.

Skills

Reinforcement Learning

Control Systems

Robot Simulation

Python

C++

Education

Master’s or PhD in Robotics, Control Systems, Machine Learning

Tools

Mujoco

Isaac Sim

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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.

We’re seeking a highly skilled Senior Reinforcement Learning (RL) Control Engineer to develop locomotion and whole body control skills for our humanoid robots. You’ll be at the cutting edge of robotics, responsible for developing advanced control algorithms that balance precision, efficiency, and adaptability. This role focuses on designing robust controllers for walking, balancing while manipulating, fall recovery, and other advanced mobility tasks. We’re seeking candidates with deep expertise in reinforcement learning and a strong track record of deploying control systems on physical robots.

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.

Key Responsibilities:

Design and implement RL-based control policies for locomotion tasks, including walking, balancing while manipulating, squatting, stair climbing, fall recovery, and other dynamic maneuvers.
Model and simulate complex dynamics, taking into account robot kinematics, actuator limitations, and environmental interactions to optimize performance.
Conduct rigorous testing in both simulated and real-world environments to validate algorithms and ensure robustness across various conditions.
Collaborate closely with software and perception teams to integrate control strategies into the full-stack robotic system.

Required Qualifications:

Master’s or PhD in Robotics, Control Systems, Machine Learning, or a related field.
At least 3+ years of experience in the design and implementation of control systems for legged robots, focusing on locomotion.
Strong expertise in reinforcement learning applied to robotics
Deep understanding of humanoid robot dynamics.
Proven and strong experience with hardware-in-the-loop testing and deployment on physical legged robots.
Strong hands-on experience with robot simulation platforms such as Mujoco, Isaac Sim or similar environments.
Proficiency in Python and C++ for algorithm development, testing, and deployment.
Experience in topics like model-free RL, imitation learning, or hybrid control systems that combine classic and modern methods.

Preferred Qualifications:

Experience with sensor fusion for state estimation (IMUs, joint encoders, force/torque sensors).
Understanding of actuators dynamics and modeling, and limitations.
High competitive salary.
23 working days of vacation per year.
Opportunity to work on the latest technologies in AI, Robotics, Blockchain and others.
Startup model, offering a dynamic and innovative work environment.

Seniority level

Seniority level
Mid-Senior level

Employment type

Employment type
Full-time

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