Research Scientist, RL for Autonomous Planning & World Modeling

About the role

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 AI Foundations team develops machine‑learning solutions for autonomous driving, tackling open problems such as reinforcement learning, learning from demonstration, generative modeling, Bayesian inference, hierarchical learning, and robust evaluation. The team collaborates with other research groups across Alphabet. This hybrid role reports to a Principal Scientist.

Responsibilities

• Participate in Waymo’s Foundation World Model post‑training and evaluation.
• Research and develop cutting‑edge reinforcement‑learning and distillation techniques for autonomous‑vehicle trajectory planning.
• Integrate emerging research from the broader AI community into Waymo’s internal RL infrastructure, conducting rigorous ablations to identify and scale the most promising methods.
• Partner with engineering and research teams across Waymo to share recipes, techniques, and post‑training best practices to accelerate collective know‑how.

Requirements

• PhD or Master’s in Computer Science, Machine Learning, Robotics, or a related technical field.
• 3+ years of industry or post‑doc research experience in Reinforcement Learning or Foundation Models.
• Demonstrated original contributions to the field through high‑impact publications (ArXiv, peer‑reviewed conferences such as NeurIPS, ICLR, CVPR), technical blog posts, or significant open‑source contributions.
• Proficiency in implementing model‑training flows in a scalable, distributed, and performant manner (e.g., data parallel, FSDP, other sharding approaches).
• Willingness to work with the complexity of globally distributed inference infrastructure.

Preferred qualifications

• PhD with a research focus on Reinforcement Learning, Foundation Models, or Multi‑Modal learning.
• Extensive experience designing and deploying reinforcement‑learning infrastructure, especially for on‑policy learning or alignment with human preferences.
• Consistent history of original contributions to the AI community, evidenced by first‑author publications at top‑tier venues (e.g., NeurIPS, ICLR, ICRA) or maintaining significant open‑source ML projects.
• Experience with large‑scale (many‑machine) training infrastructure and techniques for inference with large models such as model sharding or tensor‑parallelism.

Benefits

• Health, dental, vision, life, and disability insurance.
• Retirement benefits: 401(k) with company match.
• Paid time off: 20 days of vacation per year (accruing at 6.15 hours per pay period for the first five years).
• Sick time: 40 hours/year (statutory where applicable) plus discretionary 5 days/event.
• Maternity leave (short‑term disability + baby bonding): 28–30 weeks.
• Baby bonding leave: 18 weeks.
• 13 paid holidays per year.
• Comprehensive benefits package available to all eligible U.S.‑based employees.
• Remote work may be limited in certain locations; discuss preferred remote arrangement with the recruiter.

Pay

Base salary range: $204,000 — $259,000 USD (actual starting pay depends on location, experience, training, and skill level). Employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and the broader Company benefits program, subject to eligibility requirements.