Reinforcement Learning Engineer
4 weeks ago
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. Our fast-paced environment demands high levels of precision, organization, and execution—not just in engineering, but across all functions.
Position Overview
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.
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, 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
Required Skills and Qualifications
- 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
Preferred Experience (Bonus)
- 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
Job Details
- 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
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