Machine Learning Engineer: Imitation and Reinforcement Learning for Robotics
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’re looking for a Machine Learning Engineer with a focus on behavior learning, specifically data-driven behavior policies and robust data infrastructure. In this role, you'll be responsible for developing and scaling state-of-the-art learning architectures, while also building the data systems that make these models reliable, scalable, and reproducible in production.
What You’ll Do:
Design, train, validate, and launch models for behavior cloning and reinforcement learning
Build and maintain data ingestion, labeling, and management pipelines to ensure high-quality training datasets
Build metrics to evaluate model performance in open loop, simulation, and in the real world
Collaborate with simulation, systems, and infrastructure teams to integrate ML models into real-world autonomous systems
Deploy and debug these models in real-world environments, addressing practical issues such as latency, hardware constraints, and system integration
What We’re Looking For:
3+ years of practical experience applying Machine Learning with Deep Learning frameworks, such as PyTorch/Tensorflow/JAX to solve real-world problems
3+ years of professional experience building, deploying, and maintaining Machine Learning models in production environments
Familiarity with recent literature and methods in learned behavior policies
Practical experience in behavior cloning and/or reinforcement learning
Bonus: Experience with diffusion policies, Vision-Language-Action (VLA) models, or related technologies
Bonus: Published work in conferences such as ICRA, IROS, CoRL, CVPR, ECCV, ICCV, ICML, NeurIPS, …
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.