Senior Camera Pipeline and Image Quality Engineer

Join the team bringing advanced autonomy to the built world. At Bedrock, we’re moving AI out of the lab and into the real world. Our team is composed of industry veterans who helped launch Waymo, scaled Segment to a $3.2B acquisition, and grew Uber Freight to $5B in revenue. Today, we’re deploying autonomous systems on heavy construction machinery across the country, accelerating project schedules of billion-dollar infrastructure projects and improving safety on job sites.

Backed by $350M in funding, we’re working quickly to close the gap between America's surging demand for housing, data centers, manufacturing hubs, and the construction industry's growing labor shortage.

This is where algorithms meet steel-toed boots. You’ll collaborate with construction veterans and world-class engineers to solve physical-world problems that simulations can’t touch. If you're ready to apply cutting-edge technology to solve meaningful problems alongside a talented team— 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 senior 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.

• 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, time stamping, 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

• 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…