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.
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, TD3)
Build reward functions, curriculum learning strategies, and simulation environments tailored for real-world transfer
Design multi-agent training pipelines, including distributed rollouts, experience replay, and adaptive difficulty scaling
Interface with Isaac Gym, Mujoco, Brax, and custom physics simulators to run large-scale experiments
Work with hardware and firmware teams to deploy trained policies to embedded or real-time environments
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
Bachelor's or Master's degree in Computer Science, Engineering, Robotics, or a related field
2+ years of hands-on experience applying deep reinforcement learning to simulation or robotic control tasks
Strong grasp of machine learning fundamentals and control theory
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
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
Location: Cambridge, Ontario
Work Environment: In-person (on-site at our Waterloo facility)
Type: Full-time
Compensation: Competitive salary (based on experience)
Health Insurance: Provided
Growth: Regular performance evaluations with potential for salary increases and stock option participation
Reinforcement Learning Engineer (Full-Time) - Humanoid Robot • CAMBRIDGE, Ontario, Canada
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