Machine Learning Engineer: Imitation and Reinforcement Learning for Robotics

Bedrock Robotics; San Francisco (CA); Hybrid; USD 120,000 - 160,000; Full time

Job summary

A robotics company focusing on autonomy is seeking a Machine Learning Engineer to work on developing and scaling learning architectures and building data systems. In this role, you will design and deploy models for behavior cloning and reinforcement learning. The ideal candidate should have over 3 years of experience applying machine learning techniques, especially with frameworks like PyTorch and TensorFlow. Join a talented team dedicated to solving real-world problems in the construction industry.

Qualifications

• 3+ years of practical experience applying Machine Learning.
• 3+ years of experience building and deploying ML models in production.
• Familiarity with literature on learned behavior policies.

Responsibilities

• Design, train, validate, and launch models for behavior cloning.
• Build and maintain data ingestion pipelines for training datasets.
• Deploy and debug ML models in real-world environments.

Skills

Machine Learning with Deep Learning frameworks

Behavior cloning

Reinforcement learning

Data ingestion and management pipelines

Collaborative problem-solving

Tools

PyTorch

TensorFlow

JAX

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.

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.