Hexagon is seeking a Reinforcement Learning (Physical AI) Intern to pioneer innovations in high-performance computing, solver intelligence, and machine learning integration across simulation workflows, aiming to develop real-world adaptive systems and intelligent automation.
Requirements
- Experience with Python, PyTorch, and RL libraries (e.g., Stable Baselines, RLlib).
- Hands-on experience with simulators (e.g., Gazebo, MuJoCo).
- Strong math optimization and control theory background.
Responsibilities
- Develop RL prototypes using algorithms like Deep Q, PPO, and A3C for control and optimization tasks.
- Integrate RL into simulation environments and robotics simulators.
- Collaborate on adaptive systems that learn from feedback to improve performance over time.
- Support internal presentations and documentation for RL-based automation initiatives.
Other
- Pursuing a degree in Robotics, AI, Computer Science, or related field.
- Summer 2026 (10–12 weeks)
- Hybrid workplace
- Paid internship
