Robotics Engineering Intern — Hardware & Software

The posting, as Trace wrote it

ABOUT TRACE

Trace is building the data, infrastructure, and research that enable robots to learn from the real world. General-purpose robotics requires a deeper understanding of how robots learn from real-world data. Our research explores how to transform human experiences, including egocentric video, demonstrations, hand-object interactions, and multimodal sensory signals, into transferable representations, actionable knowledge, and generalizable robot behaviors. At our Brooklyn research lab, we combine fundamental research in robot learning, computer vision, and embodied AI with large-scale datasets, machine learning infrastructure, and real-world robotic experimentation. Our goal is to advance the science of robot learning while developing the datasets and learning foundations for increasingly capable, adaptable, and reliable robots.

ABOUT THE ROLE

We are looking for highly motivated and technically curious Robotics Engineering Interns to join our team in a hands-on hardware-and-software role, contributing to the development of robotic systems, data infrastructure, and experimental platforms that support cutting-edge research in robot learning and embodied AI. This internship is designed primarily for Master's and undergraduate students, although PhD students are also welcome to apply. We value practical robotics experience, strong engineering fundamentals, problem-solving ability, and a genuine interest in robotics research. Prior research publications are not required.

As a Robotics Engineering Intern, you will work closely with our research scientists, research interns, and engineers to build and operate robotic platforms, develop reliable data collection and processing pipelines, implement and evaluate learning-based robotic systems, and support experiments that connect human and robot data to downstream policy performance. You will have opportunities to take ownership of well-defined engineering and experimental projects, from hardware and software integration through implementation, testing, evaluation, and documentation. Depending on your background and interests, you may also contribute to research experiments, algorithm development, and technical publications. Our goal is to provide meaningful hands-on experience at the intersection of robotics engineering and robot learning research, while giving students opportunities to develop practical skills, deepen their research understanding, and contribute to advances in physical AI.

PROJECTS YOU MAY WORK ON

Depending on your experience and interests, your projects may involve one or more of the following areas:

• Robotic Hardware and System Integration
• Multimodal Data Collection and Infrastructure
• Data Processing, Calibration, and Quality Evaluation
• Human Demonstration and Robot Learning Systems
• Robot Policy Implementation and Evaluation
• Simulation and Experimental Validation

We welcome students with diverse technical backgrounds. You do not need prior ex
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e robots, or humanoid robots, including hardware integration, control, or experimental deployment.

• Familiarity with ROS/ROS 2, robot communication interfaces, motion planning, or robotic control software.
• Experience with cameras, depth sensors, tactile sensors, force/torque sensors, motion capture systems, or multimodal sensor integration.
• Experience with data acquisition, sensor calibration, temporal synchronization, data processing, or large-scale robotics datasets.
• Familiarity with robot learning methods such as imitation learning, reinforcement learning, diffusion policies, or vision-language-action models.
• Experience with machine learning frameworks such as PyTorch or JAX, including training, fine-tuning, or evaluating learning-based models.
• Experience with robotics simulation tools such as MuJoCo, Isaac Sim, or related environments.
• Experience with CAD, mechanical prototyping, 3D printing, electronics, or embedded systems.
• Previous experience in an academic robotics lab, robotics competition, engineering internship, or substantial independent robotics project.
• Contributions to open-source robotics software, technical projects, datasets, or research publications are a plus, but not required.

HOW TO APPLY

Please submit your resume/CV along with 1-3 examples of relevant technical or research work, such as GitHub repositories, project pages, robotics demonstration videos, open-source contributions, or publications. We encourage applicants to briefly describe their personal contributions to these projects, their technical interests, and what they hope to learn or accomplish during the internship.

Please indicate whether you are interested in a summer internship, a part-time internship during the academic year, or a full-time co-op opportunity. Internship start dates are flexible. Academic credit may be available if required by your degree program, subject to institutional requirements.

Trace is an equal opportunity employer.