Research Intern

Trace is hiring Research Interns to advance research at the intersection of robot learning, embodied AI, and learning from human experience. Interns will lead focused research projects, develop and evaluate methods, run experiments with real-world data and robotic platforms, and contribute to research publications and artifacts.

Responsibilities:

• Lead an original research project from problem formulation and hypothesis development through method design, implementation, experimentation, and evaluation, with guidance and collaboration from Trace's research team
• Develop and evaluate novel robot learning methods, including learning algorithms, representations, models, or training strategies that leverage human demonstrations, multimodal datasets, and robotic interaction data
• Design rigorous experiments and benchmarks to investigate fundamental questions about data quality, human-to-robot transfer, policy learning, generalization, and real-world robotic performance
• Work with diverse data modalities, potentially including egocentric video, human hand and body motion, teleoperation demonstrations, visual-tactile signals, force measurements, and robot trajectories
• Collaborate with research scientists and engineers to build reproducible experimental pipelines, train and evaluate models, and validate research findings in simulation and/or on real robotic platforms
• Analyze experimental results and failure modes, draw actionable scientific insights, and communicate findings through technical discussions, presentations, and written reports
• Contribute to high-quality research publications and open research artifacts, with opportunities to submit work to leading robotics and machine learning venues