Camera Pipeline and Image Quality Engineer
Bedrock Robotics · San Francisco
Join the team bringing autonomy to the built world
At Bedrock, we're moving AI out of the lab and onto real job sites. Our team includes veterans who helped launch Waymo, scaled Segment to a $3.2B acquisition, and grew Uber Freight to $5B in revenue. We retrofit the heavy equipment contractors already own, starting with excavators, so experienced crews can safely take on more projects and deliver more of the critical infrastructure our society needs.
In August 2026, Bedrock-equipped excavators began doing fully autonomous, paid work on live commercial job sites. We've more than doubled our customer base since 2025, and our machines now work with contractors across the South, Midwest, and Mid-Atlantic
In October 2026, we raised a $600M Series C co-led by CapitalG and Emergence Capital, bringing our total funding to nearly $1B. We're using it to put more autonomous machines to work, build the field and operations teams behind every deployment, and expand into new machine types and job-site tasks.
This is where algorithms meet steel-toed boots. You'll work alongside construction veterans and world-class engineers to solve physical-world problems that simulations can't touch. If you're ready to do meaningful work on hard problems, we'd love to have you join us.
We are a group of veterans from the autonomous vehicle industry who are passionate about bringing the benefits of automation to areas in the construction industry currently underserved by the market. Cameras power our autonomy stack on rugged construction machines across all lighting conditions, from full sun to complete darkness, in scenes with high dynamic range, dust, glare, and unpredictable site lighting. We are looking for a Camera Pipeline and Image Quality Engineer to own the full camera pipeline: from embedded drivers and data interfaces through ISP tuning, ensuring our cameras deliver usable imagery for both ML perception models and human teleoperation across a wide range of lighting conditions.
Key Qualifications
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
Responsibilities
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
Education and Experience
Bachelor's or Master's degree in Electrical Engineering, Computer Engineering, Optical Engineering, Physics, or a related field
8+ years of experience in embedded camera systems, ISP tuning, image quality engineering, or closely related roles with a strong pipeline focus
Demonstrated ability to characterize, diagnose, and improve camera image quality on fielded hardware
Embedded systems experience: comfort at the hardware/software boundary, driver-level debugging, and working with real-time constraints on embedded compute platforms
Comfortable working in C/C++ and/or Python for driver-level work, tooling, and test automation
Ways to Stand Out From the Field
Experience diagnosing and correcting AE anchoring and HDR tone mapping failures in scenes with extreme intra-frame contrast: retroreflective surfaces, direct artificial light sources, or simultaneous deep shadow and bright highlights
Experience with construction, off-road, automotive, or other outdoor autonomous/robotic platforms operating in harsh environments (dust, vibration, wide temperature range, direct sunlight and full dark)
Experience with automotive-grade high-speed camera interfaces (GMSL, FPD-Link, MIPI CSI-2) and embedded compute platforms (e.g. Nvidia Jetson/Orin) in a production or near-production deployment context
Experience with camera systems that must simultaneously serve human viewing (teleoperation/remote assistance) and ML/perception model consumption
Familiarity with co-designing active illumination systems (NIR/visible, pulsed/continuous) alongside ISP tuning
Familiarity with IEC 60825-1 eye safety analysis for machine-mounted illuminators
Bedrock Robotics is an Equal Opportunity Employer
We’re committed to building a diverse and inclusive workplace. We consider all qualified applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, ancestry, age, disability, veteran status, genetic information, or any other protected characteristic.
Reasonable Accommodations
We want our hiring process to be accessible to everyone. If you need an accommodation to participate in the application or interview process, please let your recruiter know so we can support you.
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