ML Engineer, Manipulation

Responsibilities

• Develop learning-based manipulation models for end to end sensor-driven interaction (e.g., reaching, motion generation, and execution in dynamic environments).
• Build and maintain manipulation training pipelines: dataset creation from robot logs/teleop, action representations, augmentation, and distributed training.
• Design evaluation metrics and regression tests that quantify manipulation reliability, recovery behavior, and safety in real environments.
• Develop sim-to-real workflows for manipulation learning, including simulation environments, domain randomization, and failure-mode testing.
• Optimize and distill models for edge deployment; benchmark latency, memory use, and stability on target hardware.
• Partner with the AI platform team to integrate policies with control and safety systems, and validate end-to-end performance on robots.
• Analyze field performance, identify dominant failure modes, and drive iterative improvements through data collection and targeted retraining.

Qualifications

• Bachelor’s or Master’s degree in Robotics, Computer Science, Electrical Engineering, or related field (PhD a plus).
• 3+ years of experience applying ML to robotics manipulation, visuomotor control, or sequential to sequence models.
• Strong proficiency in PyTorch and experience building reliable training/evaluation pipelines.
• Strong software engineering skills in Python; ability to collaborate across ML and robotics teams.