Tech Stack

Description

You will drive the design, training, validation, and deployment of contact-rich manipulation skills for real-world manufacturing, focusing on dexterous assembly tasks. You will develop sensor-based control strategies and ML policies within the Intrinsic Robot Control stack, managing the full model lifecycle to meet industrial standards.

Requirements

• PhD or equivalent experience in Robot Learning and AI Manipulation
• 2+ years professional experience in C++ and Python with production-quality code
• Deep expertise with JAX, TensorFlow, or PyTorch
• Direct experience testing on physical robots with vision and force-torque sensors
• Business fluency in English

Responsibilities

• Develop, train, and deploy multi-modal feedback and interaction controllers for high-precision assembly tasks
• Lead the AI manipulation model lifecycle, conducting trials on industrial hardware
• Architect modular components for reinforcement learning and imitation learning
• Stress-test and optimize real-time execution framework for learned manipulation models