Hardware Engineer Intern, Camera & Sensing Architecture
MetaDevelop and validate first-principles models of camera imaging performance (e.g., SNR across illumination, MTF/resolution, dynamic range, motion blur, depth of field) for candidate sensor and lens configurations.
Run imaging and sensor-level trade studies (resolution vs. pixel size, rolling vs. global shutter, RGB vs. mono vs. IR, fixed-focus vs. AF, frame rate vs. power) and summarize results as clear, data-backed recommendations.
Help define metrics and figures of merit that link feature KPIs (photo quality, text/OCR legibility, ML model accuracy, tracking robustness, latency) to camera HW specifications.
Correlate performance models against sensor and module characterization data, identifying and explaining gaps between predicted and measured performance.
Build reusable modeling and analysis tools, and document assumptions and methods so the architecture team can apply them to future products.
Communicate findings, assumptions, and risks to architects, cross-functional partners, and team leadership.
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