Job description

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 experience in every area, and projects will be matched to your skills, interests, and available opportunities. WHAT YOU’LL LEARN AND BUILD - Build and integrate robotic systems, working with robot arms, dexterous hands, sensors, cameras, and associated software and hardware interfaces. - Develop and maintain robotics software and data pipelines for data acquisition, synchronization, processing, storage, visualization, and experimental evaluation. - Support robot learning research experiments by implementing existing methods, preparing datasets, running model training or evaluation, and executing experiments in simulation and/or on real robotic platforms. - Design and execute systematic tests to assess hardware reliability, sensor accuracy, data quality, policy performance, and experimental reproducibility. - Debug and troubleshoot complex robotic systems, identifying issues across hardware, perception, control, data processing, and learning components. - Analyze experimental results and failure cases, helping the research team understand how system design, data collection, and implementation choices affect robotic learning and performance. - Collaborate with researchers and engineers to improve experimental workflows, prototype new capabilities, and translate research ideas into functioning robotic systems. - Document implementations, experimental procedures, and results to support reproducibility, knowledge sharing, and continued development. WHAT WE'RE LOOKING FOR - Currently pursuing a Bachelor's, Master's, or PhD degree in Robotics, Computer Science, Electrical Engineering, Mechanical Engineering, Computer Engineering, Mechatronics, or a closely related field. - Solid programming skills in Python and/or C++, with experience developing, testing, or debugging software for robotics, machine learning, or related technical applications. - Foundational understanding of robotics concepts, such as robot kinematics, control, perception, sensing, or robotic system integration. - Hands-on experience with robotics hardware, simulation, or substantial robotics software projects, gained through coursework, research labs, internships, personal projects, or robotics competitions. - Strong practical problem-solving skills, willingness to learn unfamiliar tools and technologies, and ability to troubleshoot technical challenges independently. - Good communication and collaboration skills, attention to technical detail, and enthusiasm for working in a fast-paced, interdisciplinary research and engineering environment. NICE TO HAVE - Experience with robotic platforms such as manipulators, dexterous hands, mobile 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.

Requirements

Must-haves

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 experience in every area, and projects will be matched to your skills, interests, and available opportunities.
Build and integrate robotic systems, working with robot arms, dexterous hands, sensors, cameras, and associated software and hardware interfaces.
Develop and maintain robotics software and data pipelines for data acquisition, synchronization, processing, storage, visualization, and experimental evaluation.
Support robot learning research experiments by implementing existing methods, preparing datasets, running model training or evaluation, and executing experiments in simulation and/or on real robotic platforms.
Design and execute systematic tests to assess hardware reliability, sensor accuracy, data quality, policy performance, and experimental reproducibility.
Debug and troubleshoot complex robotic systems, identifying issues across hardware, perception, control, data processing, and learning components.
Analyze experimental results and failure cases, helping the research team understand how system design, data collection, and implementation choices affect robotic learning and performance.

Nice-to-haves

Experience with robotic platforms such as manipulators, dexterous hands, mobile 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.

Application prep

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Keywords to mirror

aimachine learningpython

Platform: ashby
Effort: quick

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