Systems Validation Engineer

Highbrow Technology Inc; Sunnyvale (CA); Sur place; USD 120 000 - 160 000; Plein temps

Résumé du poste

Highbrow Technology Inc. seeks a Systems Validation Engineer to own multi-sensor validation for next-generation wearables.

You will build test capability for HW image quality impacting on-device AI features, characterizing camera performance and developing setups, methods and automation. The role emphasizes image-quality expertise applied through targeted studies and cross-functional optimization rather than routine validation, with hands-on optical lab work and pre-production hardware

Qualifications

• Masters degree with 6–10 years of experience in imaging science, computer vision, ISP, or related field.
• Demonstrated hands-on validation of consumer imaging systems against objective image quality metrics.
• Strong Python skills and experience building test automation and image/video analysis tooling.

Responsabilités

• Design and build validation methods for performance across vision and sensing AI use cases.
• Build and own the test capability: lab setups, lighting, charts, fixtures and calibration.

Connaissances

Python

Image quality testing

Lab measurement

Test automation

Pre-production hardware

Formation

Masters Degree in Imaging Science/Computer Science/Image Processing/Computer Vision/Optics/Electrical Engineering

Outils

OpenCV

Description du poste

We are seeking a Systems Validation Engineer to own multi-sensor (e.g. vision/audio) system validation for next-generation wearables. The core of the role is building test capability that does not yet exist, especially for HW image quality that feeds on-device AI features. A significant part of the job is characterizing camera hardware performance in ways that predict whether those AI features will work, then building the setups, methods and automation to measure it. Established image-quality expertise is the foundation you bring — applied through targeted studies and cross-functional optimization rather than routine metric-by-metric validation.

Responsibilities

• Design and build validation methods for performance across vision and other sensing AI use cases — establishing the distances, environmental 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 gesture, audio, 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 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 multii-camera combined performance and interactions/coexistence under environmental and system stress, including temperature, ambient brightness extremes and thermally or power-constrained operation.
• Plan and execute system-level camera validation across photo, video and streaming capture in service of the AI use cases above; apply classic objective image-quality metrics (e.g. MTF) through targeted DOEs and cross-functional optimization support, rather than routine full-coverage validation runs.
• Document methods and results so that tests can be repeated and results defended by others.

Qualifications

• Masters Degree with 6-10 years of experience and background in Imaging Science, Computer Science, Image Processing, Computer vision, Optics, Electrical Engineering, or a related field, with substantial relevant industry experience in ISP, computer vision or Image Quality Testing.

Required Skills

• Demonstrated hands-on experience validating consumer imaging 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.
• Working knowledge of camera geometric calibration — interpreting and applying an intrinsic/distortion calibration file to dewarp imagery (e.g. with OpenCV), and familiarity with camera–IMU and cross-sensor temporal synchronization.

Preferred Skills

• 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 SLAM / visual-inertial (6DoF) localization, gaze/eye-tracking or handtracking — including the geometric-accuracy and calibration considerations these systems depend on, and 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.