Waymo
Research Scientist, RL for Autonomous Planning & World Modeling
$204K–$259K
Mountain View, CA, USA +7 · Full-time · Posted Aug 29, 2026
- Location
- Mountain View, CA, USA
San Francisco, CA, USA
Kirkland, WA, USA
New York City, NY, USA
Kirkland, Washington, United States
Mountain View, California, United States
New York, New York, United States
San Francisco, California, United States - Experience
- 3+ years of industry or postdoctoral research experience in Reinforcement Learning or Foundation Models.
- Education
- PhD or Master’s in Computer Science, Machine Learning, Robotics, or a similar technical field.
About the job
Research and develop reinforcement learning methods for autonomous vehicle trajectory planning, and contribute to post-training and evaluation of Waymo’s Foundation World Model.
What you'll do
- Participate in Foundation World Model post-training and evaluation.
- Develop reinforcement learning and distillation techniques for autonomous vehicle trajectory planning.
- Integrate and test research in Waymo’s RL infrastructure and share methods with research and engineering teams.
What they ask for
- PhD or Master’s in Computer Science, Machine Learning, Robotics, or a similar technical field.
- 3+ years of industry or postdoctoral research experience in Reinforcement Learning or Foundation Models.
- Demonstrated original contributions through publications, technical blog posts, or significant open-source contributions.
- Ability to implement scalable, distributed, performant model-training flows and willingness to work with globally distributed inference infrastructure.
Nice to have
- PhD focused on Reinforcement Learning, Foundation Models, or Multi-Modal learning.
- Experience designing and deploying RL infrastructure, large-scale training, or large-model inference techniques.
Pay
$204,000 — $259,000 USD (base salary)
Tools
Data parallel, FSDP
AI focus
Research and develop reinforcement learning and distillation for autonomous vehicle trajectory planning; post-train and evaluate a foundation world model; integrate and rigorously evaluate emerging AI research.
Model-written summary · the original posting is authoritative