Machine Learning Engineer: Robotics ML Generalist
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.
Bedrock is bringing autonomy to the construction industry! We’re 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.
We’re looking for a highly motivated engineer with experience deploying machine learning algorithms to physical systems in the real world. The ideal candidate has hands-on experience in perception (e.g., object detection, semantic segmentation, depth estimation) and/or behavior learning methods (e.g., Vision-Language-Action (VLA) models, diffusion policies). More importantly, you’ve shipped ML models to robots in production environments, and you understand the complexities that come with it.
What you’ll do:
Develop and optimize real-time ML models for edge deployment on robotic systems
Work with vendors to label data and build robust data extraction and labeling pipelines
Design custom metrics to evaluate model performance in the field
Reduce model latency using tools like ONNX, TensorRT, or similar
What we’re looking for:
Practical experience applying machine learning with deep learning frameworks, such as PyTorch, to solve real-world problems
Proficiency in Python and comfort with at least one systems language (e.g., C++, Rust)
Experience deploying ML models to robotic systems or other physical platforms
Experience incorporating raw sensor data like camera, lidar, radar, IMUs, etc into deep learning algorithms.
Bonus: Practical application of incorporating 3D geometry into deep learning models
Bonus: Published work in conferences such as ICRA, IROS, CoRL, CVPR, ECCV, ICCV, ICML, NeurIPS,
***We’re especially interested in engineers who thrive at the intersection of ML research and real-world robotics applications.
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.