Research Engineer, Robotics & Embodied AI

Meta Reality Labs Research (RL Research) is a team of researchers and engineers pushing the frontier of AI, robotics, and AR/VR technology. Within RL Research, our team is building the next generation of foundation models (VLAs, WAMs) for robotic manipulation. We are looking for a skilled Research Engineer to take those models onto real hardware in our lab.
This role covers the full robotics stack: hardware bring-up, teleoperation, data collection, and running learned policies on real robots. It involves building prototypes together with research scientists and engineers, running the experiments that show whether they work, and finding the problems that only appear once a model has to act in the physical world.

Responsibilities

Build, operate, and maintain robot platforms for teleoperation and deployment
Deploy learned policies on real robots and optimize their performance on-robot
Work with researchers to design experiments and measure how well policies work on real hardware
Debug failures across hardware, perception, data, and policies
Turn research results into working prototypes
Build tooling that makes experiments repeatable
Publish research results in top conferences and release code that contributes to the field of robotics

Qualifications

Currently has, or is in the process of obtaining, a PhD degree in robotics, computer vision, machine learning, or a related field
Industry experience with modern, general-purpose robot platforms
Hands-on experience bringing up, calibrating, and debugging a new robot platform from scratch
Experience running learned policies on real robots and improving their performance
First-author publications at ICRA, CoRL, RSS, IROS, or CVPR, or widely used open-source contributions
Proficient in Python and PyTorch Contributed to open-source robot learning projects such as LeRobot, robosuite, or ManiSkill
Trained or evaluated large 3D generative or world models
Built teleoperation rigs and used them to collect robot data at scale
Debugged policy failures across hardware, data, training, and simulation
Worked on long-horizon planning for robots
Worked on 3D computer vision for robots, such as reconstruction, pose estimation, or scene understanding
Worked on contact-rich manipulation, including force or tactile feedback
Trained or fine-tuned vision-language-action models or world action models
Fluent in ROS or ROS 2, including kinematics and camera-to-robot calibration