Research Member of Technical Staff- Robot Learning Data

What You'll Do

• Design and implement teleoperation and demonstration collection systems for high-quality robot learning data
• Develop data quality metrics, curation pipelines, and filtering strategies specific to robotic interaction data
• Research methods to augment real robot data with synthetic, simulated, or cross-embodiment sources
• Identify and source external robotic datasets to expand training diversity across platforms and tasks
• Build tooling for researchers to explore, annotate, and iterate on robotic datasets
• Collaborate with pre-training and post-training teams to translate model data needs into concrete collection strategies
• Measure the downstream impact of data collection decisions on model and policy performance

What We're Looking For

• Hands-on experience with robotic data collection, teleoperation systems, or demonstration frameworks
• Understanding of what makes robot learning data useful: diversity, coverage, temporal quality, and action fidelity
• Strong software engineering skills for building reliable data collection and processing systems
• Ability to reason across hardware, pipelines, and model performance
• Experience working with real robotic hardware in a research or industrial setting

Nice To Have (But Not Required)

• Experience with sim-to-real transfer and synthetic data generation for robotics
• Familiarity with cross-embodiment datasets (e.g., Open X-Embodiment, DROID)
• Experience with VR teleoperation, motion capture, or dexterous demonstration collection
• Understanding of imitation learning and how data properties affect policy generalization
• PhD or strong research background in robotics or ML