Machine Learning / Reinforcement Learning Engineer

What you'll do

• Research and implement reinforcement and supervised learning algorithms; develop simulation models and domain randomization strategies.
• Design experiments to optimize policy performance and sample complexity under real-time constraints, and build scalable pipelines for real and synthetic data.
• Deploy, evaluate, and debug policies on physical hardware, identifying bottlenecks and improving performance with the robotics team.

What they ask for

• BS, MS, or PhD in Computer Science, Robotics, or a related field.
• Deep theoretical and practical knowledge of reinforcement learning and supervised learning algorithms.
• Experience with physics engines and robotics middleware; expert-level Python and proficiency in PyTorch or JAX.
• Understanding of Transformers, CNNs, and Foundation Models; commitment to clean code, version control, and reproducible workflows.

Nice to have

• Publications in top-tier robotics or machine learning conferences, or a portfolio demonstrating practical expertise.

Tools

Isaac Sim, MuJoCo, PyBullet, ROS/ROS2, Python, PyTorch, JAX, Transformers

AI focus

Develop reinforcement and supervised learning algorithms for robotic manipulation, including simulation, data-efficient training, and deployment on physical robots.