System Validation Engineer

Position: System Validation Engineer (Multimodal AI / Camera)

Location: Sunnyvale, CA (Onsite)

Exp: 5-12 years

Must Haves: Image Quality validation, Camera hardware characterization, AI, system validation, Optics, Python

We are seeking a Systems Validation Engineer to own image quality and vision based system validation for smart glasses and next-generation wearables. This role spans two kinds of work. The first is established image quality validation, and we expect you to arrive able to do it independently across the full range of standard measurements across the full imaging pipeline. The second is building test capability that does not exist yet, especially for HW image quality that feed on-device AI features for real world applications. A significant part of this role is figuring out how to characterize camera hardware performance in ways that predict whether those AI features will work, and then building the setups, methods and automation to measure it. 

What you will do

• Plan and execute system-level camera validation across photo, video and streaming capture, covering the full range of objective image quality and capture performance metrics.
• Design and build validation methods for camera hardware performance across vision AI use cases such as object recognition, text reading and code recognition — establishing the distances, lighting conditions and scene types over which the hardware supports each feature, and characterizing where and how it fails.
• Build and own the test capability itself: specify and assemble lab setups, controlled lighting scenarios, test targets and charts, opto-mechanical fixtures and motion rigs, and keep them calibrated, documented and repeatable.
• Develop Python automation to take testing from one-off manual measurements to high-volume, repeatable runs — device control and data capture, batch image and video analysis, metric extraction and automated reporting.
• Where a requirement or limit is still open, propose the measurement method, the pass/fail approach, and the data volume needed to make the result credible.
• Investigate and root-cause image quality and capture issues on pre-production hardware, and produce clear, actionable reports for hardware and software teams, with sound judgment on the conclusions and step forward.
• Validate camera performance under environmental and system stress, including temperature, ambient brightness extremes and thermally or power-constrained operation.
• Document methods and results so that tests can be repeated and results defended by others.

Minimum qualifications

• Degree in Imaging Science, Optics, Electrical Engineering, Computer Science, Image Processing or a related field, with substantial relevant industry experience in ISP or camera validation.
• Demonstrated hands-on experience validating consumer camera systems against objective image quality metrics, with working knowledge of industry image quality test protocols, charts and evaluation tools.
• Practical understanding of how vision AI features consume camera output — enough to design tests that expose camera-side limitations, and to reason about accuracy, false detections, and the conditions under which a feature degrades. Model development experience is not required.
• Strong Python, with demonstrated experience building test automation and image or video analysis tooling, not only running existing scripts.
• Hands-on optical lab capability: creating, aligning, calibrating and maintaining sensitive measurement setups, working with controlled illumination, targets, opto-mechanics and motion control components.
• Comfortable working on pre-production hardware: device bring-up, flashing, shell scripting, log capture and scripted data acquisition.
• Strong analytical judgement and clear reporting. You can explain a measurement, and defend it or revisit it and drive towards clarity when your result is challenged.
• Able to work with ambiguity, incomplete specifications and shifting priorities, and to juggle competing requests from a large cross-functional team.

Preferred qualifications

• Experience validating camera-driven perception or AI features on an embedded or wearable device.
• Understanding of system-level interactions across the imaging pipeline — sensor, optics, ISP, and the downstream consumers of image data.
• Experience building ground-truth test sets and reasoning about sample size and statistical confidence in validation results.
• Exposure to gaze or eye-tracking, or hand-tracking, validation — including test design involving human subjects and inter-subject variability.
• Experience with subjective and perceptual image quality evaluation alongside objective metrics.
• Familiarity with image sensor and optics hardware development and the associated evaluation methodologies.
• Prototyping skill with imaging test targets, custom scene setups and device interface fixtures.
• Basic familiarity with optical simulation or mechanical CAD tools for designing test rigs.