Robotics Software Engineer Vision-Language-Action Models

About the role

This role is a Robotics Software Engineer specializing in robot learning and manipulation. The focus is on solving dexterous, contact-rich manipulation tasks.

Responsibilities

• Develop, train, and deploy multimodal VLA and learned manipulation policies that solve contact-rich tasks, integrating sensor-based control strategies that combine vision, force-torque, and tactile feedback.
• Run the manipulation model lifecycle, conducting regular trials on real hardware to evaluate algorithmic changes and curate high-quality training datasets.
• Architect and build modular components for imitation and reinforcement learning, continuously improving the robustness of contact-rich tasks.
• Create pipelines for continual learning, enabling policies to keep improving from new demonstrations and real-world deployment data over time.
• Stress-test and optimize the real-time execution framework for learned policies running on the robot.

Requirements

• A Master’s or PhD degree in robotics, computer science, machine learning, or a related field, or equivalent professional experience in robot learning and manipulation (e.g., visuomotor policy learning, imitation learning, applied reinforcement learning).
• Professional experience in Python and C++, with a proven track record of shipping production-quality code.
• Deep expertise with a modern ML framework (PyTorch preferred; JAX or TensorFlow also welcome).
• Direct experience testing and iterating on physical robots integrated with vision, force-torque, and tactile sensors.
• Experience with robotic simulation environments (MuJoCo, Isaac Sim, Drake, or similar).

Qualifications

• You thrive in the lab, spending significant time running hardware experiments and troubleshooting real-world edge cases.
• Business fluency in English.

Skills that will differentiate your candidacy

• Experience manipulating and analyzing complex, large-scale, high-dimensional multimodal data from varying sources, with proficiency training models at scale on cloud-based workflows.
• Experience with edge deployment and model optimization for real-time control: low-latency inference, quantization, distillation, and pruning to meet tight control-loop rates on resource-constrained, on-robot hardware and accelerators.
• Contributions to robot learning through publications or open-source projects.
• Familiarity with ROS 2 and hands-on experience using industrial and collaborative robots from vendors such as Universal Robots, ABB, Fanuc, and KUKA.
• Experience with multibody dynamics simulators such as Adams.