over 1 year ago
Boston, MA, USASenior
Responsibilities
- Research and implement reinforcement learning and supervised learning algorithms for robotic manipulation.
- Design simulation models and domain randomization strategies aligned with physical robotic systems.
- Evaluate and optimize model architectures for sample complexity and policy performance under real-time execution constraints.
- Develop scalable data management pipelines for real and synthetic data.
- Deploy, evaluate, and debug learned policies on physical hardware and improve performance with the robotics team.
Requirements
- 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 such as Isaac Sim, MuJoCo, or PyBullet and robotics middleware such as ROS or ROS2.
- Deep understanding of Transformers, CNNs, and Foundation Models.
- Expert-level Python skills and proficiency with PyTorch or JAX.
- Commitment to clean code, version control, and reproducible experimental workflows.
- Publications in top-tier robotics or machine learning conferences, or a practical project portfolio demonstrating strong expertise.
Categories
ML EngineeringRobotics
