About the role
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
What they're looking for
- 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
More about this role
At Bedrock, we're moving AI out of the lab and into the real world. Our team includes 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 equipment across the country, improving safety on job sites and accelerating schedules on critical infrastructure projects.
We're not here debating the future of AI. We're deploying it in the real world. In just two years, we've raised $350M and achieved the first fully autonomous excavator deployments in construction.
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...
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