Research Scientist, Robot Learning - Continual Adaptation

Responsibilities

• Design and implement methods for adapting generative robot policies using techniques such as reinforcement learning, imitation learning, agentic tool use, and related methods.
• Develop methods that enable generative robot policies to improve through ongoing interaction with people and the environment, using signals such as demonstrations, interventions, preference feedback, learned rewards, and other human-driven signals.
• Develop rollout-based learning and exploration strategies that enable robots to efficiently and safely gather useful experience, leveraging multimodal data from demonstrations, teleoperation, interventions, and on-policy rollouts. In both simulation and on real robot hardware.
• Collaborate across teams in perception, control, foundation models, and ML infrastructure to deploy scalable and reproducible research systems.
• Publish research outcomes and contribute to the open robotics and embodied AI communities.

Qualifications

• Ph.D. or M.S. in Robotics, Machine Learning, Computer Vision, or a related field, or equivalent applied research experience.
• Strong background in reinforcement learning, imitation learning, or closely related robot-learning methods.
• Strong proficiency with deep learning frameworks such as PyTorch or JAX and robotics simulation environments such as IsaacLab or MuJoCo.
• Experience with policy adaptation, continual learning, preference learning, or related learning problems in embodied settings.
• Strong coding and experimental skills with an emphasis on reproducibility and rigorous evaluation.
• Prior robotics experience with real-world hardware and ML-based robot deployments.
• Publications in top-tier conferences such as CoRL, RSS, NeurIPS, ICLR, ICML, ICRA, or CVPR.

Bonus Qualifications

• Experience with human-in-the-loop robot learning (e.g., DAgger), preference learning, reward learning, or RLHF/RLAIF.
• Prior work on generative robot policies or VLAs such as 0.5, GR00T, or related models.
• Experience building or managing robot data collection or replay infrastructure.
• Familiarity with real-world robot platforms such as YAMs, Kinova, humanoids, or mobile manipulators.

Additional information

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