Research Scientist, RL for Autonomous Planning & World Modeling

The Waymo AI Foundations team is seeking a hybrid Research Scientist to contribute to the development of machine learning solutions addressing open problems in autonomous driving. This role will involve researching and developing cutting-edge Reinforcement Learning (RL) and Distillation techniques for Autonomous Vehicle Trajectory Planning. You will also 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.

About the Role

You will report to a Principal Scientist and collaborate with engineering and research teams across Waymo to share recipes, techniques, and post-training best practices to accelerate our collective knowledge. You will work on the Waymo Foundation World Model post-training and evaluation research, focusing on RL and Distillation techniques for Autonomous Vehicle Trajectory Planning.

Responsibilities

• Participate in Waymo’s Foundation World Model post-training and evaluation research
• Develop cutting-edge RL and Distillation techniques for Autonomous Vehicle Trajectory Planning
• Integrate emerging research from the broader AI community into Waymo’s internal RL infrastructure
• Conduct rigorous ablations to identify and scale the most promising methods
• Collaborate with engineering and research teams across Waymo to share recipes, techniques, and post-training best practices

Requirements

• PhD or Masters in Computer Science, Machine Learning, Robotics, or a similar technical field with 3+ years of industry or post-doc research experience in Reinforcement Learning or Foundation Models
• Demonstration of original contributions to the field through high-impact publications (ArXiv, peer-reviewed conferences like NeurIPS/ICLR/CVPR), technical blog posts, or significant open-source contributions
• Proficiency in implementing model training flows in a scalable, distributed, and performant manner such as Data parallel, FSDP, and other sharding approaches
• A willingness to work with the complexity of globally distributed inference infrastructure

Qualifications

• PhD in Computer Science, Machine Learning, or Robotics, with a research focus on Reinforcement Learning, Foundation Models, or Multi-Modal learning
• Extensive experience designing and deploying Reinforcement Learning infrastructure, specifically for on-policy learning or alignment with human preferences
• A 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/tensor-parallel

Skills

• Strong background in Reinforcement Learning and related fields
• Experience with large-scale training infrastructure and techniques for inference with large models
• Ability to design and deploy RL infrastructure for on-policy learning or alignment with human preferences
• Experience with multi-modal learning and foundation models

Benefits

In accordance with Washington state law, Waymo offers a comprehensive benefits package, including:

• Health, dental, vision, life, disability insurance
• Retirement Benefits: 401(k) with company match
• Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment
• Sick Time: 40 hours/year (statutory, where applicable); 5 days/event (discretionary)
• Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks
• Baby Bonding Leave: 18 weeks
• Holidays: 13 paid days per year