Senior Camera Pipeline, Image Quality Engineer

Bedrock Robotics

Senior Camera Pipeline Engineer optimizing imaging systems for autonomous construction. Collaborating with cross-functional teams to enhance image quality on rugged construction machinery.

Posted 7/29/2026full-timeSan Francisco • California • 🇺🇸 United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in tuning ISP pipelines and embedded camera driver development, with a strong focus on image quality optimization and data analysis. Capable of managing relationships with vendors and translating technical requirements into actionable ISP tuning targets.

Highest-signal resume keywords

ISP Pipeline TuningEmbedded Camera Driver DevelopmentImage Quality CharacterizationData Analysis SkillsCamera Sensor Fundamentals

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

ISP Parameter TuningAuto ExposureAuto White BalanceTone MappingDemosaicingNoise ReductionHDR FusionHistogram AnalysisStatistical MethodsQuantitative Modeling

Tools & Technologies

V4L2MIPI CSI-2GMSLI2CROS2

Industry Keywords

Image Quality EngineeringEmbedded Camera SystemsCamera Data PipelinesRadiometryPhoton Transfer Curves

About the role

Key responsibilities & impact

Own the camera pipeline end to end: diagnose image quality failures, tune ISP parameters, and validate improvements across the full operating radiometric range from bright daylight to night with machine-mounted illumination
Own embedded camera driver development and integration: register-level control, frame synchronization, and software interfaces that expose runtime ISP parameter control to the autonomy stack
Characterize existing ISP pipeline behavior from first principles: identify root causes of image quality failures using parameter-level access, histogram analysis, raw vs processed frame comparison, and controlled test scenes
Tune ISP and imager settings (exposure, white balance, HDR sub-frame ratios, tone mapping, noise reduction) to optimize image quality for both ML perception models and remote assistance/teleoperation
Establish and run test protocols that confirm ISP changes improve low-light performance without adversely affecting existing daytime model performance or training data compatibility
Work closely with perception, autonomy, and sensing engineering to translate scene and platform constraints (yaw rates, lux levels, detection ranges) into concrete ISP tuning targets
Debug camera issues from sensor/ISP register state through to captured imagery, both in the lab and in the field
Define and drive image quality characterization methodologies (SFR/MTF, noise, dynamic range, photon transfer curves) and track performance across hardware and ISP firmware generations
Manage relationships with ISP, camera module, and embedded compute vendors

Requirements

What you’ll need

Hands-on experience tuning ISP pipelines on real hardware (auto exposure, auto white balance, tone mapping, demosaicing, noise reduction, and HDR fusion) with a track record of diagnosing and correcting failure modes such as AE anchoring on bright point sources, aggressive HDR sub-frame ratio compression, and tone mapping that crushes scene content in mixed-light environments
Deep familiarity with AE algorithm internals: histogram weighting, metering zone selection, exposure ratio control in multi-exposure HDR pipelines, and lux estimation, and how these interact with scenes containing simultaneously very bright and very dark content
Hands-on experience writing or integrating embedded camera drivers (V4L2, MIPI CSI-2, GMSL, I2C) and building the tooling and software interfaces that expose ISP and imager control to an autonomy software stack
Familiarity with camera data pipelines on embedded platforms: frame synchronization, timestamping, compression, bandwidth management, and integration with autonomy middleware (ROS2 or similar)
Understanding of how ISP tuning choices affect downstream ML/perception model performance, and experience validating that pipeline changes do not degrade existing model behavior
Working knowledge of camera sensor fundamentals (CMOS architecture, shutter types, CFA patterns, dynamic range, sensitivity, and binning) sufficient to reason about how sensor choice and configuration interact with ISP behavior
Working knowledge of radiometry sufficient to interpret photon budget models, SNR predictions, and motion-blur constraints as inputs to ISP tuning requirements
Strong data analysis skills, including experience working with large datasets, building quantitative models, and using statistical methods to characterize real-world system behavior
8+ years of relevant industry experience in ISP tuning, embedded camera systems, image quality engineering, or closely related roles.

Benefits

Comp & perks

Our roles are often flexible. If you don't fit all the criteria, or are in another location (especially one where we have an office like SF or NY) please apply anyway! We'd love to consider you.