Bedrock Robotics
Posted 1mo ago

Machine Learning Engineer: Perception Analytics

Bedrock Robotics
San Francisco, California, United States
HybridFull Time
Responsibilities
  • developing perception
  • deploying models
  • defining scope
Requirements
  • MSc or advanced degree in CS/Robotics,4+ years shipping perception systems,proficiency with PyTorch and Python,experience with C++ or Rust and raw sensor (camera,lidar,IMU) pipelines,3D geometry and sensor calibration
Technical tools mentioned
PyTorchPythonC++Rust

Job description

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.

Machine Learning Engineer: Perception Analytics

We are looking for a perception engineer to build features for our customers' analytics. This isn’t just a data science job. This is developing fundamental perception features to drive customer-facing platforms. What are the real-world challenges in measuring dig productivity from lidar/camera data? Can we modify existing perception systems to provide useful metrics for job sites? How do you measure safety over an entire site given perception systems? How do you re-identify the same objects across dozens of camera views on a site several city blocks wide, through dust and changing light?

What You’ll Do:

  • Develop perception algorithms for re-id of vehicles on massive construction sites

  • Extend, modify and adapt existing perception signals for analytics needs

  • Work with customers to define and scope what is possible with advanced perception systems on-site

  • Deploy models and analytics directly to fleets of machines

What We're Looking For:

  • MSc or advanced degree in Computer Science, Robotics, or a related field

  • 4+ years of professional experience shipping perception systems to production (ideally on robotic or other embedded platforms), with strong hands-on experience in a modeling framework (e.g. PyTorch)

  • Proficient in Python and comfortable reading and writing at least one systems language (e.g. C++, Rust)

  • Hands-on experience incorporating raw sensor data (camera, lidar, IMU) into learned pipelines

  • Solid grounding in 3D geometry, sensor calibration (intrinsics/extrinsics), coordinate transforms, and camera/image re-projection algorithms

  • Strong data analysis skills across statistical characterization of sensor data, corner-case and anomaly discovery, and evaluation design

Ways to Stand Out:

One or more of the following:

  • Prior experience with object re-ID tasks

  • Prior experience working with customers to understand perception data and analytics

  • Experience with the Rust programming language

  • Published work in top-tier venues such as ICRA, IROS, CVPR, ECCV, ICCV, CoRL, or RSS

Other special aspects of the role:

  • Based in the Bay Area with the ability to be onsite at our SF office on a daily/weekly basis

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.

About Bedrock Robotics

Autonomous construction technology that retrofits heavy equipment into autonomous machines for general contractors.

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