MLE, Post-Training & Reinforcement Learning Frameworks at WAYMO LLC

Mountain View, California, United States -

Full Time

Start Date

Immediate

Expiry Date

12 Jan, 26

Salary

259000.0

Posted On

14 Oct, 25

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Reinforcement Learning, Distributed Systems, TensorFlow, JAX, XLA, Python, C++, Profiling Tools

Industry

technology;Information and Internet

Description

Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. The Waymo ML Frameworks & Efficiency team partners closely with Research and Production teams across Waymo to develop and deploy 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. They are geared towards both scaling models with efficiency and solving problems unique to ML for autonomous driving. 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. You’ll work closely with researchers and modeling engineers across the company, and tackle the challenges of large-scale reinforcement learning (RL), building systems that can scale across compute, data, and environments to improve model intelligence and alignment with human drivers. You Will: Develop the core training system for adapting RL techniques to unprecedented scales and heterogeneous environments (i.e. CPU/GPU/TPU). Collaborate with cross-functional teams to integrate cutting-edge rollout strategies, policies, and RL algorithms (i.e. REINFORCE, DPO, PPO, etc.) into the system. Optimize 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 robust 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 initiatives. You Have: 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. We Prefer: MS in Computer Science, Math Familiarity with post-training frameworks like TS/REX, Tunix, TorchRL, TRL, etc. The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Salary Range $204,000—$259,000 USD

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Responsibilities

Develop the core training system for adapting reinforcement learning techniques to unprecedented scales and heterogeneous environments. Collaborate with cross-functional teams to integrate cutting-edge rollout strategies and optimize the end-to-end RL training pipeline.