Robotics Software Engineer, Robot Learning and Manipulation

Role

As a Robotics Software Engineer specializing in Robot Learning and Manipulation, you will drive the design, training, validation and deployment of contact-rich manipulation skills for real-world manufacturing. Your focus will be on solving dexterous, contact-rich assembly tasks. You will develop and integrate advanced sensor-based control strategies and Machine Learning (ML) policies within the Intrinsic Robot Control stack, managing the full model lifecycle - from architecture, large-scale data collection and training to validation, deployment and cycle-time optimization - to meet rigorous industrial standards.

How your work moves the mission forward

• Develop, train, and deploy multi-modal feedback- and interaction controllers to solve high-precision insertion and assembly tasks.
• Lead the AI manipulation model lifecycle, conducting regular trials on industrial hardware to evaluate algorithmic changes and curate high-quality training datasets.
• Architect and build modular components for reinforcement learning and imitation learning, continuously seeking to improve the robustness of contact-rich assembly tasks.
• Stress-test and optimize the real-time execution framework for learned manipulation models within the Intrinsic platform.

Skills you will need to be successful

• PhD or equivalent professional experience in Robot Learning and AI Manipulation (e.g., Applied RL, Visuomotor Policy Learning, Vision-Language-Action models).
• 2 years of professional experience in C++ and Python, with a proven track record of shipping production-quality code.
• ML Frameworks: Deep expertise with JAX, TensorFlow, or PyTorch.
• Hardware Experience: Direct experience testing and iterating on physical robots integrated with vision and force-torque sensors.
• Operational Grit: A desire to spend significant time in the lab, managing hardware experiments and troubleshooting physical-world edge cases.
• Communication: Business fluency in English.

Skills that will differentiate your candidacy

• Multimodal Perception and Control: Expertise in integrating tactile/force sensing and computer vision for contact-rich tasks.
• Hardware Experience: Direct experience testing and iterating on physical robot manipulators integrated with vision and force-torque sensors.
• Large-Scale Training: Proficiency in training on massive multimodal datasets and managing cloud-based training workflows (e.g. Argo).
• Infrastructure: Experience with cloud-based training (Argo) and deploying inference on the edge.
• Customer Focus: Willingness to travel internationally to analyze customer use cases and deploy solutions under ambitious timelines.