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ashby

CV/ML Camera Test Engineer

Specter
San Francisco, USA
Full-time
Junior · 2+ years experience
Onsite
Discovered 1 weeks ago
gitlinuxnumpyopencvpandaspython

Full Job Posting

Qualifications:

  • 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.

Nice to Have

  • 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.
  • Tool Proficiency
  • 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).

Responsibilities

  • 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.

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