Reinforcement Learning Engineer (Full-Time) - Humanoid Robot

CA·CAMBRIDGEmid

OtherEngineer

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Quick Summary

Overview

About AXIBO AXIBO is a robotics company pioneering the design, prototyping, and manufacturing of advanced robotic systems—all under one roof. We build everything in-house and take pride in delivering robust, reliable products that power automation across industries.

Key Responsibilities

Develop reinforcement learning agents for robotic control tasks such as locomotion, manipulation, and dynamic balance Implement learning architectures using policy gradient methods, actor-critic frameworks, and off-policy algorithms (e.g., PPO, SAC,…

Technical Tools

cpppythonpytorchtensorflowdeep-learninglinuxmachine-learning

AXIBO is a robotics company pioneering the design, prototyping, and manufacturing of advanced robotic systems—all under one roof. We build everything in-house and take pride in delivering robust, reliable products that power automation across industries. Our fast-paced environment demands high levels of precision, organization, and execution—not just in engineering, but across all functions.

As a Reinforcement Learning Engineer, you will develop and deploy machine learning systems that enable intelligent behaviors in our humanoid and legged robots. You'll work at the intersection of control theory, deep learning, and robotics—helping close the loop between simulation and reality to bring adaptive behaviors into real-world machines.

Responsibilities

~1 min read

Design diagnostic tools and visualization dashboards to monitor training progress and system behavior

Apply domain randomization, sim2real techniques, and sensor noise modeling to enhance policy robustness

Maintain code quality through version control, testing, and modular design

Stay current with academic literature and integrate novel RL methods as appropriate

Requirements

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Bachelor's or Master’s degree in Computer Science, Engineering, Robotics, or a related field

Proficiency with PyTorch, JAX, or TensorFlow

Programming experience in Python and C++

Deep understanding of policy optimization, generalization, and environment design

Experience working in Linux development environments and with GPU-based training pipelines

Excellent debugging skills across ML, software, and hardware stacks

Ability to independently manage experiments and rapidly iterate on model architectures

Nice to Have

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Deployment of RL systems to real-world robots, especially legged or humanoid platforms
Contributions to open-source RL frameworks or robotics middleware (e.g., ROS, Isaac ROS)
Experience with imitation learning, behavior cloning, or inverse reinforcement learning
Prior research/publications in reinforcement learning, multi-agent systems, or robotic control
Familiarity with low-level robot interfaces, sensor fusion, or control loop tuning
Knowledge of real-time systems, embedded software, or custom actuator control