Senior Reinforcement Learning Engineer

Skills

About the Role

You will lead the design, training, deployment, and maintenance of reinforcement-learning policies for robot motion. You will improve sim-to-real pipelines and ML tooling, mentor engineers, investigate field failures, strengthen policy robustness, and develop efficient Python and C++ software for the learning and locomotion stack.

Requirements

• PhD in robotics, machine learning, computer science, or a related field with a reinforcement-learning focus, or equivalent experience
• Alternatively, a Master’s degree and 5+ years of professional experience
• Experience deploying and maintaining ML models in the field
• Knowledge of robot motion control, state estimation, path planning, and actuation
• Experience with Gazebo or Isaac Sim
• Knowledge of sim-to-real transfer, domain randomization, reward shaping, and policy robustness
• Proficiency in Python and PyTorch
• Working knowledge of C++
• Knowledge of Linux systems and middleware frameworks
• Excellent English communication skills

Responsibilities

• Lead the design, training, and deployment of reinforcement-learning policies for robot motion
• Provide technical guidance and mentor engineers on learning-based control
• Own and improve the RL training infrastructure and sim-to-real pipeline
• Shape ML tooling and experiment-management workflows
• Collaborate with stakeholders to expand autonomous robot operations
• Triage locomotion field issues and improve policy robustness
• Write, deploy, and maintain Python and C++ software for learning and locomotion

Benefits

• Employee stock ownership plan