Machine Learning Engineer: Imitation and Reinforcement Learning for Robotics

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.

- 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

- 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.