CV/ML Camera Test Engineer
- Level
- Mid
- Work type
- On-site · San Francisco
Not stated in the posting: salary, visa.
Open. First seen 15 October 2026.
About the role
Specter's standard text
Company Background
Specter's mission is to help automate the physical world.
Today, we build video sensors with state-of-the-art AI agents that answer any question, anywhere in their environments. Our systems can automatically detect and reason about any physical activity captured on camera, from security incidents (e.g. perimeter intrusion, theft, LPR), to safety monitoring (e.g. PPE detection, injured people), to operational efficiency (e.g. material tracking, congestion monitoring). We offer both long range wireless (1km range) and wired sensor variants to suit any deployment.
Our co-founders Xerxes and Philip are passionate about empowering our partners in the fast approaching world of physical AI and robotics. We are a small, fast growing team who hail from Anduril, Tesla, Uber, and the U.S. Special Forces.
The Role
Specter is hiring a hands-on CV/ML camera test engineer to measure how camera and optics choices affect the performance of our AI models. Working with our Optics & Camera Engineering Lead, this individual will turn experiment plans into lab and field setups, run the campaigns, push the data through our CV/ML models and report what changed and why. The work is scientific at its core: reason from first principles about optics, sensor physics and illumination, form a hypothesis, design a controlled experiment to test it, and let the data decide. This individual will collaborate with camera, CV/ML, software, hardware and field teams to put data behind every camera decision.
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.
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).
What this role asks for
Particular to this role
Bash · Git · Machine Learning · NumPy · Pandas
Rare in Specter's other 30 open postings.
Standard for QA / testing roles
Python
Common in QA / testing postings at any company.
82% of this description is written for the role. The rest is Specter's standard text, folded away below.
Read against Specter's other open postings on this site.
The team: Engineering HIRES STEADILY
This role is one of 22 open in Engineering. 2 were posted in the last 30 days and the oldest in October: a team that hires all year rather than one being built.
WHAT THE TEAM WORKS WITH, ACROSS ITS POSTINGS
Python (15 of 22), Embedded Systems (12 of 22), Linux (12 of 22), Computer Vision (8 of 22), Rust (8 of 22), Golang (6 of 22), C (5 of 22)
OTHER ROLES ON THIS TEAM
- Optics & Camera Engineering Lead posted Oct 16
- Software Engineer - Applied AI posted Aug 28
- Software Engineer — Fleet posted Aug 24
- Platform Site Reliability Engineer posted Aug 20
- Hardware Test Engineer posted Aug 5
- Hardware Test Lead posted Aug 5
- Engineering Lab Manager posted Jul 14
- Engineering Technician posted Jul 14
- Fleet Reliability Engineer posted Jul 13
- Site Reliability Engineer posted Jul 2
- Data Operations Engineer posted May 20
- Software Engineer - Test Automation posted Mar 31
- Software Engineer - Embedded Systems posted Jan 23
- Software Engineer - Full Stack posted Jan 13
- RF Systems Engineer posted Nov 27
- Software Engineer - ML Infrastructure posted Oct 3
- Software Engineer - Systems posted Oct 3
- Software Engineer - Backend posted Oct 3
- Mechanical Engineer posted Oct 3
- Electrical Engineer posted Oct 3
- ML Research Engineer posted Oct 3
From the team and dates on Specter's own job board.
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