Hardware Engineer Intern, Camera & Sensing Architecture

The Camera Architecture team within Camera, Depth & Sensors (CDS) at Reality Labs defines the imaging hardware architecture for Meta's AI glasses, AR glasses, VR headsets, wearable devices, and Physical AI / Robotics platforms. We sit at the intersection of image sensor technology, optics, and the AI/imaging feature stack — translating product vision into camera hardware solutions that ship  cameras serve photography, computer vision (SLAM, hand/eye tracking, passthrough), and contextual-AI features.

As a Hardware Engineer Intern on the Camera Architecture team, you will help build the quantitative foundation behind camera architecture decisions. Working alongside camera architects and sensor, lens, module, and AI teams, you will develop first-principles models and run trade studies that connect camera hardware parameters — resolution, pixel size, SNR, MTF, dynamic range, frame rate, shutter type, power — to the product features they enable.

Depending on project and background, your work may also extend into how camera hardware choices affect on-device computer vision and AI performance. You will own a well-scoped project end to end, from problem definition through analysis and a recommendation presented to the team.

This is a 12-week internship.

Currently has, or is in the process of obtaining, an MS or PhD degree in Electrical Engineering, Optical Engineering, Physics, Computer Engineering, Computer Science, or a related technical field Coursework or research experience in one or more of: imaging systems, optics, image sensors, signal/image processing, or computational photography Experience with physics-based modeling, simulation, and data analysis in Python or MATLAB Must obtain work authorization in country of employment at the time of hire, and maintain ongoing work authorization during employment Intent to return to full-time degree program after completion of the internship Understanding of CMOS image sensor fundamentals (noise sources, quantum efficiency, full well, shutter types, HDR) and camera module components Familiarity with image quality metrics (MTF, SNR, dynamic range), ISP pipelines, or color science Experience training or evaluating computer vision / ML models, particularly their sensitivity to input image quality Research impact demonstrated through publications, a thesis, or projects in imaging, optics, sensors, or computational imaging Experience communicating technical results to both technical and non-technical audiences