Member of Technical Staff, Robotics & Embodied AI Research

• Lead frontier robotics research across VLA models, generalist policies, world models, and emerging embodied AI approaches.
• Design end-to-end experimentation systems spanning teleoperation, data collection, simulation, policy deployment, and real-world evaluation.
• Develop high-fidelity simulations using real-world data to evaluate robot policies and validate performance in physical environments.
• Leverage flow, our human-in-the-loop annotation platform, to structure and scale robotics datasets from egocentric video, teleoperation demonstrations, and multimodal sensor data.
• Investigate model limitations, failure modes, and sim-to-real gaps to develop improved datasets, benchmarks, and evaluation methodologies.
• Rapidly prototype and validate novel approaches to robot learning, policy optimization, and embodied intelligence.
• Independently identify high-impact research opportunities and lead projects from initial exploration through experimentation and scalable implementation.

• Strong background in robotics, embodied AI, robot learning, computer vision, or applied machine learning research.
• Hands-on experience developing, training, or evaluating robot learning models, including VLA models, imitation learning, or generalist policies.
• Proficiency in Python and experience with robotics or ML frameworks such as PyTorch, ROS, or simulation environments.
• Experience designing robotics experiments involving real-world data collection, simulation, policy evaluation, or hardware deployment.
• Understanding of robot learning methodologies, multimodal perception, and sim-to-real transfer challenges.
• Ability to analyze model behavior, identify failure modes, and translate research findings into measurable performance improvements.
• Strong research ownership, experimental judgment, and ability to independently execute complex technical projects.
• Strong technical communication and collaboration skills across research and engineering teams.

• Experience with world models, reinforcement learning, imitation learning, or diffusion-based robot policies.
• Hands-on experience with teleoperation, robotics hardware, sensor integration, or real-world policy deployment.
• Familiarity with robotics simulators such as MuJoCo, Isaac Sim, or comparable environments.
• Experience building multimodal robotics datasets from egocentric video, robot trajectories, or sensor data.
• Prior experience at a leading robotics or frontier AI research lab.
• Contributions to robotics research publications, open-source frameworks, or benchmark development.

Project and contracting process: the hiring partner. Applications continue on the provider's website.