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San Francisco, California, United States
Onsite
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.
The Mission:
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.