Reinforcement Learning Engineer (Full-Time) - Humanoid Robot

AXIBO INC; Cambridge; On-site; CAD 80,000 - 120,000; Full time

Job summary

AXIBO INC is seeking a motivated Reinforcement Learning Engineer to develop intelligent systems for robots. You will engage in enhancing robotic behaviors using advanced machine learning techniques, working with cutting-edge technologies in a collaborative environment. Ideal candidates will possess robust experience in RL applications and a background in computer science or engineering.

Benefits

Health Insurance Provided

Regular Performance Evaluations

Salary Increases Potential

Stock Option Participation

Qualifications

• 2+ years of hands-on experience with reinforcement learning in robotics.
• Deep understanding of reinforcement learning algorithms.
• Experience in managing experiments and iterating model architectures.

Responsibilities

• Develop reinforcement learning agents for robotic control tasks.
• Implement learning architectures using advanced RL methods.
• Monitor training progress and system behavior through diagnostics.

Skills

Deep Reinforcement Learning

Machine Learning Fundamentals

Control Theory

Python

C++

Debugging Skills

GPU-based Training Pipelines

Policy Optimization

Environment Design

Education

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

Tools

PyTorch

JAX

TensorFlow

Linux Development Environments

Job description

Job Description

Job Description

About AXIBO

AXIBO is a robotics company pioneering the design, prototyping, and manufacturing of advanced robotic systemsall 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 roboticshelping 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