Research Scientist Intern, Robotics

About the Role

Meta is seeking a Research Scientist Intern to advance the state of the art in robot learning and embodied intelligence. This role involves building systems that perceive, reason, and act in the physical world. Internships are twelve (12) to twenty-four (24) weeks long with various start dates.

Key Responsibilities

• Develop novel state-of-the-art algorithms for robot perception, manipulation, navigation, and control, leveraging modern deep learning and foundation-model techniques.
• Train and evaluate robot learning policies—including imitation learning, reinforcement learning, and vision-language-action models—in simulation and on physical hardware.
• Analyze and improve the efficiency, robustness, scalability, and real-time performance of algorithms deployed on robot platforms.
• Perform research to advance the science and technology of intelligent machines.
• Perform research that enables learning the semantics of multimodal and embodied data (images, video, depth, point clouds, tactile and force signals, proprioception, language, audio).
• Contribute to data collection, teleoperation, and sim-to-real transfer pipelines that support large-scale robot learning.
• Collaborate with researchers and cross-functional partners across hardware, controls, software, and product.
• Communicate research plans, progress, and results.
• Publish research results and contribute to research that can be applied to Meta product development.

Requirements & Tech Stack

• Experience with Python, C++, C, Java, or other related languages.
• Experience building systems based on machine learning and/or deep learning methods.
• Must obtain work authorization in country of employment at the time of hire and maintain ongoing work authorization.

Preferred Qualifications

• Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences such as RSS, CoRL, ICRA, IROS, NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, or similar.
• Experience working and communicating cross functionally in a team environment.
• Experience in advancing AI techniques for robotics or computer vision, including core contributions to open source robotics or ML libraries and frameworks (e.g., ROS/ROS 2, MuJoCo, Isaac Sim/Lab, Drake, PyBullet, PyTorch).
• Experience deploying learned policies or perception systems on real robot hardware—manipulators, mobile robots, humanoids, or dexterous hands.
• Experience with simulation, domain randomization, and sim-to-real transfer.
• Experience with classical robotics foundations: state estimation, SLAM, calibration, motion planning, and optimal or model-predictive control.
• Publications or experience in robot learning, reinforcement learning, machine learning, AI, computer vision, optimization, computer science, statistics, applied mathematics, or data science.
• Experience solving analytical problems using quantitative approaches.
• Experience setting up ML or robot experiments and analyzing their results, including real-world evaluation protocols.
• Experience manipulating and analyzing complex, large scale, high-dimensionality data from varying sources and sensor modalities.
• Experience in utilizing theoretical and empirical research to solve problems.
• Experience with deep learning frameworks.