Software Engineer - Manipulation

Job description

Job responsibilities

• Implement and deploy state-of-the-art learning-based manipulation policies to achieve reliable, world-class performance on real-world tasks with physical hardware.
• Adapt and improve policies for new tasks and hardware using post-training methods such as RL fine-tuning, policy distillation, and interactive imitation learning (e.g., DAgger).
• Build and maintain the data pipelines that support post-training, including teleoperation and demonstration collection, retargeting, dataset aggregation, and curation.
• Drive the development cycle from simulation and offline evaluation to robustly transferring and fine-tuning policies on the robot.
• Develop visuomotor policies for grasping, in-hand manipulation, and whole-arm manipulation, with reliable, low-latency execution on hardware.
• Define evaluation metrics and regression tests for policy performance in simulation and on hardware, and use the results to guide technical decisions.
• Collaborate closely with the simulation, data, hardware, and systems teams to diagnose system-level issues and co-develop solutions.
• Maintain high standards for the codebase through testing, documentation, and peer code reviews.

Job requirements

• Manipulation: Strong experience in robotic manipulation, including grasping, in-hand or whole-arm control, and high-DOF motion, with a track record of hardware deployment.
• Learning-Based Control: Hands-on experience training and debugging learned policies, including post-training methods such as RL fine-tuning, distillation, and imitation learning (e.g., DAgger).
• Software Engineering: Proficiency in Python, C++, and PyTorch or JAX, with experience building real-time robotic software stacks.
• Data & Evaluation: Experience working with demonstration and teleoperation data, and building evaluation pipelines for learned policies.
• An MS with 3+ years of relevant experience, or a recent PhD, in Robotics, Computer Science, Electrical Engineering, or a related field.
• Relevant experience specifically focused on learning-based manipulation or a closely related area.
• A proven track record of taking learned policies from a research/simulation environment and successfully deploying them on physical hardware.
• Prolonged periods of sitting at a desk and working on a computer