CV/ML Camera Test Engineer

Form testable hypotheses from first principles (optics, sensor physics, radiometry and illumination), and design controlled experiments with defined variables, controls, sample sizes and success criteria before collecting data.

Execute experiments comparing sensors, lenses, ISP settings, IR illumination, focus and window conditions, and measure their effect on detection, recognition and LPR performance at range.

Build and maintain lab and outdoor test setups, including targets at known distances, controlled lighting and low-light scenes, IR illumination and chamber runs across temperature.

Run field data-collection campaigns across sites, times of day, lighting and weather, keeping conditions, metadata and ground truth consistent.

Run captured video through existing CV/ML models and evaluation tools, and compute model metrics alongside image-quality measurements (MTF, SNR, distortion, pixels on target).

Script captures, camera settings, data pipelines and analysis in Python and shell on embedded Linux devices so every run is reproducible.

Isolate the cause of performance changes across optics, sensor, ISP, illumination and environment, using statistical analysis rather than anecdote, and write clear reports that state confidence, limitations and recommendations.

Support camera validation (focus stability, thermal, condensation, IR range) through EVT, DVT and PVT.

Comfortable working in a fast-paced environment and being scrappy.

Bachelor's Degree in Electrical Engineering, Computer Engineering, Physics, Mechanical Engineering, Computer Science, or equivalent.

2+ years of experience in hardware test, validation or experimental work.

Strong Linux command line proficiency (required), including SSH, logs, file and process management and shell scripting on embedded devices.

Proficient in Python for scripting, data processing and analysis.

A scientific, first-principles mindset: you reason from the physics to predict results, design controlled experiments to test those predictions, and change your mind when the data says so.

Hands-on with test setups and lab equipment, and comfortable doing outdoor field work, including at night and in bad weather.

Strong grounding in statistics and experimental design, including hypothesis testing, confidence intervals, variance, sample sizing and trends across campaigns.

Exceptional organization and communication skills (both written and oral).

No CV/ML background required. You'll use our existing models and evaluation tools and learn the rest on the job.

Experience with cameras, image sensors, lenses or image-quality testing (MTF, SNR, low light, IR).

Exposure to computer vision tools (OpenCV) or ML evaluation metrics such as precision and recall.

Experience with video tooling such as FFmpeg, GStreamer or V4L2.

Experience with thermal chambers, reliability or production test.

Background in physics, optics or a research lab environment.

Linux command line and shell scripting (required).

Python with NumPy, pandas and plotting libraries.

Git for scripts and test procedures.

Imatest or equivalent image-quality tools (preferred).