Research Intern

The role

Research Interns will lead original research projects by formulating questions, developing methods, and designing experiments. Work involves using real-world datasets, simulation, and robotic platforms to advance robot learning and embodied AI.

What you will do

• Lead an original research project from formulation through evaluation
• Develop and evaluate novel robot learning methods
• Design rigorous experiments and benchmarks
• Work with diverse data modalities including egocentric video and tactile signals
• Collaborate with scientists and engineers to build experimental pipelines
• Analyze experimental results and failure modes
• Contribute to high-quality research publications and open research artifacts

Required qualifications

• Currently pursuing a PhD or Master's degree in Robotics, Machine Learning, Computer Science, Computer Engineering, Electrical Engineering, Mechanical Engineering, or a closely related field
• Strong research background in at least one relevant area such as robot learning, imitation learning, reinforcement learning, computer vision, embodied AI, foundation models, dexterous manipulation, mu
• Demonstrated research excellence and potential evidenced by high-quality publications, impactful research projects, open-source contributions, or other substantial research accomplishments
• Excellent programming skills in Python
• Hands-on experience with modern deep learning frameworks such as PyTorch or JAX
• Strong communication, collaboration, and problem-solving skills

Nice to have

• First-author or co-first-author publications
• Experience with C++ and parallel programming, such as CUDA
• Experience with real-world robot learning experiments, robotic manipulation platforms, or deployment of learned policies on physical robots
• Experience with egocentric video, human demonstrations, hand tracking, pose estimation, motion retargeting, or cross-embodiment learning
• Experience with vision-language-action models, diffusion-based policies, imitation learning, reinforcement learning, or world models
• Experience with tactile sensing, force estimation, multimodal sensor fusion, or contact-rich manipulation
• Experience with robotics simulation environments and tools such as MuJoCo, Isaac Sim, or related frameworks
• Previous research experience in an industrial research lab or a collaborative, interdisciplinary research environment

Benefits

• Academic credit may be available if required by your degree program

About the company

Trace is building the data, infrastructure, and research that enable robots to learn from the real world. Their research explores transforming human experiences into transferable representations and generalizable robot behaviors.