About the job
Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. The Waymo ML Frameworks & Efficiency team partners with Research and Production teams across Waymo to develop models in Perception and Planning that are core to our autonomous driving software. We help our partners by offering the best frameworks for the entire model development lifecycle, including pre-training and post-training. We are looking for engineers with ML system expertise to help us train and improve pre-trained models to be deployed into Waymo Driver, and potential future products.
Responsibilities
Report into the Head of ML Frameworks & Efficiency
Develop the core training system for adapting RL techniques to unprecedented scales and heterogeneous environments (i.e. CPU/GPU/TPU).
Collaborate with teams to integrate the latest rollout strategies, policies, and RL algorithms (i.e. REINFORCE, DPO, PPO) into the system.
Improve the end-to-end RL training pipeline for efficient and scalable learners/actors, and low-latency distributed reply buffers for persisting data produced by the rollouts.
Build evaluations, analyze experimental results and iterate quickly to improve model performance and training workflows.
Stay current with the latest research in RL, Vision-Language-Action (VLA) models, and World models to inform and inspire new programs.
Qualifications
Minimum
B.S. in Computer Science, Math, or 8+ years equivalent real-world experience.
Proficient in distributed systems design with an understanding of ML efficiency.
Experience with ML frameworks, including TensorFlow, JAX, XLA.
Solid programming skills in Python and C++.
Practical familiarity with profiling tools to uncover performance bottlenecks.
Preferred
MS in Computer Science, Math
Familiarity with post-training frameworks like TS/REX, Tunix, TorchRL, TRL.