Robotics Engineering Intern — Hardware & Software

Trace•New York, NY

About The Position

Trace is seeking technically curious Robotics Engineering Interns for a hands-on hardware-and-software role supporting the development of robotic systems, data infrastructure, and experimental platforms for robot learning and embodied AI research. Interns work closely with research scientists, research interns, and engineers to build and operate robotic platforms; develop data collection and processing pipelines; implement and evaluate learning-based robotic systems; and support experiments connecting human and robot data to downstream policy performance. Interns may own defined engineering and experimental projects spanning hardware and software integration, implementation, testing, evaluation, and documentation. Depending on experience and interests, projects may include 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; and simulation and experimental validation. Work may also include research experiments, algorithm development, and technical publications. The role is intended primarily for undergraduate and master's students, though PhD students are welcome to apply. Trace welcomes students from varied technical backgrounds and matches projects to their skills, interests, and available opportunities. The internship offers practical experience at the intersection of robotics engineering and robot learning research. Internship start dates are flexible; academic credit may be available subject to institutional requirements.

Requirements

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 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 a fast-paced, interdisciplinary research and engineering environment.

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; these are a plus but not required.

Responsibilities

Build and integrate robotic systems, including 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 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 of hardware reliability, sensor accuracy, data quality, policy performance, and experimental reproducibility.
Debug and troubleshoot robotic systems across hardware, perception, control, data processing, and learning components.
Analyze experimental results and failure cases to help assess how system design, data collection, and implementation choices affect robotic learning and performance.
Collaborate with researchers and engineers to improve workflows, prototype capabilities, and translate research ideas into functioning robotic systems.
Document implementations, experimental procedures, and results to support reproducibility, knowledge sharing, and continued development.