Research Intern

Trace • New York, NY

13h

About The Position

Trace is seeking highly motivated Research Interns to work on high-impact problems in robot learning, embodied AI, and learning from human experience. This internship is designed for PhD students and exceptional Master's students interested in advancing robotics and machine learning. Interns own a focused research project and work with scientists and engineers to formulate research questions, develop methods, design rigorous experiments, and evaluate results using real-world datasets, simulation, and/or robotic platforms. Projects may contribute to research at leading robotics, machine learning, and computer vision conferences. Interns can pursue original research, collaborate with an interdisciplinary team, and help shape Trace's research directions.

Research may involve learning from human experience and egocentric data; human-to-robot and cross-embodiment transfer; foundation models for robot learning; visual-tactile and multimodal robot learning; data-centric robot learning and scaling; dexterous manipulation and generalizable robot skills; and robustness, generalization, and reliable robot learning.

The role includes leading a research project from formulating the problem and hypothesis through method development, implementation, experimentation, and evaluation. Interns develop robot learning methods, design benchmarks and experiments, work with diverse data modalities, build reproducible pipelines, analyze findings, communicate results, and contribute to publications and open research artifacts. Internship start dates are flexible. Academic credit may be available if required by the student's degree program, subject to institutional requirements.

Requirements

• 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, multimodal learning, or learning from human demonstrations.
• Demonstrated research excellence and potential through high-quality publications, impactful research projects, open-source contributions, or other substantial research accomplishments.
• Excellent programming skills in Python and hands-on experience with modern deep learning frameworks such as PyTorch or JAX.
• Strong communication, collaboration, and problem-solving skills, with the ability to work effectively in a fast-paced, interdisciplinary research environment.

Nice To Haves

• Publications at leading venues such as RSS, CoRL, ICRA, IROS, T-RO, IJRR, NeurIPS, ICML, ICLR, or CVPR; first-author or co-first-author publications are a strong plus.
• 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.

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 robot learning methods, including learning algorithms, representations, models, or training strategies that use human demonstrations, multimodal datasets, and robotic interaction data.
• Design rigorous experiments and benchmarks to study data quality, human-to-robot transfer, policy learning, generalization, and real-world robotic performance.
• Work with data modalities that may include 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, derive 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Intern