The Role
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 of NY) please apply anyway! We'd love to consider you.
Join the team bringing advanced autonomy to the built world
At Bedrock, we've assembled one of the most experienced autonomous technology teams in the industry, with deep expertise scaling breakthroughs across transportation, infrastructure, and enterprise software. Our leaders helped put the first self-driving cars on public roads at Waymo, scaled systems for Segment's $3.2B acquisition, and grew Uber Freight to $5B in revenue.
While others debate the future of AI, we're deploying it in the real world. Our systems are already installed on heavy machines across the country, learning on real construction sites and working to reshape the earth with survey-grade precision and exceptional safety. This isn't a simulation—it's autonomous intelligence working on billion-dollar infrastructure projects.
In just over a year, we've raised $80M, put our equipment into the field, and established partnerships with forward-thinking contractors who are integrating our technology into their operations. 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.
Here, algorithms meet steel-toed boots. You'll collaborate with both construction veterans and experienced engineers, tackling problems where your work directly impacts how the physical world get built. If you're interested in applying cutting-edge technology to solve meaningful problems alongside a talented team—we'd love to have you join us.