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Comprehensive Resources Inc • San Jose, California, United States
Role & seniority: Sr. Camera Validation Engineer (5–8 years)
Location & work type: San Francisco, USA — full-time on-site
Imaging/validation: Imatest, DxO Analyzer, RAW viewers, ColorChecker, light boxes, HDR charts
ISP/camera tools: ISP pipelines, tuning blocks, camera HAL tools
Testing/automation: GStreamer, ADB, log analysis tools; internal tooling
Programming/scripting: Python (OpenCV, Pillow, NumPy), Bash/PowerShell
Collaboration/triage: Jira, Confluence, test case management systems
Validate complete camera systems (optics, sensors, ISP pipelines, tuning blocks, imaging algorithms) across device generations
Build/maintain product-level sanity-check test cases; perform end-to-end ISP validation, regression testing, and feature verification; automate where possible
Conduct root-cause analysis across hardware, firmware, and software imaging components; manage large-scale image/video datasets; organize results and tooling integration
Deep understanding of camera imaging systems (optics, sensors, ISP, image-quality fundamentals)
Proven product-level camera testing and image-quality evaluation
Proficiency with GStreamer for pipeline testing; Jira for bug tracking and dashboards
Strong scripting (Python or Bash) for automation; familiarity with OpenCV, NumPy
Experience with image capture tools, lab equipment, controlled lighting; RCA across blocks
Nice-to-have
Position: Sr. Camera Validation Engineer
Location: San Francisco (USA)
Exp: 5-8 years
Key skills: Imatest, DxO Analyzer, RAW viewers, ISP, ColorChecker, light boxes, HDR charts, GStreamer, ADB, internal camera HAL tools, log analysis tools, Python (OpenCV, Pillow, NumPy), Bash/PowerShell, Jira, Confluence, test case management systems Own the validation of complete camera systems at the product level, ensuring that optics, sensors, ISP pipelines, tuning blocks, and imaging algorithms work together to deliver high‑quality imaging performance. Drive end‑to‑end sanity testing, anomaly detection, tooling integration, and large‑scale data management across multiple product generations. Regards, Validate full camera systems including optics, sensor, ISP pipeline, tuning blocks, and imaging algorithms across device generations. Build and maintain product‑level sanity‑check test cases in collaboration with block owners. Execute end‑to‑end ISP pipeline validation, regression testing, and feature verification. Compare results against golden references from previous generations to identify anomalies and regressions. Perform root cause analysis across hardware, firmware, and software imaging components. Conduct structured camera testing in image labs and real‑world environments (HDR, low‑light, motion, portrait, indoor/outdoor). Capture, tag, and maintain large‑scale image and video datasets for tuning, validation, and ML training. Participate as a portrait subject when needed for camera tests and database capture. Organize test results, maintain metadata, and manage the lifecycle of the image database. Develop lightweight automation scripts to streamline test execution, data processing, and reporting. Integrate validation workflows with internal tooling such as GStreamer pipelines, Jira ticketing systems, and automated test runners. File, track, and manage issues through Jira, ensuring clear communication with cross‑functional teams. Collaborate with imaging engineers, algorithm developers, hardware teams, and QA to drive product‑level quality. 5 to 8 years of experience
Deep understanding of camera imaging systems: optics, sensors, ISP, tuning, and image‑quality fundamentals. Strong experience with product‑level camera testing and image‑quality evaluation. Proficiency with GStreamer for camera pipeline testing, streaming, and debugging. Experience using Jira for bug tracking, workflow management, and validation dashboards. Strong scripting skills in Python or Bash for automation and data processing. Familiarity with OpenCV, NumPy, and image‑processing libraries. Experience with image capture tools, lab equipment, and controlled lighting environments. Ability to perform cross‑block root cause analysis and communicate findings clearly. Strong organizational skills for managing large‑scale image datasets..