- Robot type
- Autonomous Vehicle
- Location
- Mountain ViewCaliforniaUSA
- Job type
- Artificial Intelligence
- Posted
- Aug 3, 2026
- Salary
- $251,000–$310,000 a year
Staff Research Scientist, Foundation Models Recipes
Job description
The mission of the Waymo AI Foundations team is to develop machine learning solutions addressing open problems in autonomous driving, towards the goal of safely operating Waymo vehicles in dozens of cities and under all driving conditions. As part of our work, we also initiate and foster collaborations with other research teams in Alphabet. AI Foundations areas that we are currently focusing on include reinforcement learning, learning from demonstration, generative modeling, Bayesian inference, hierarchical learning, and robust evaluation.
In this hybrid role, you will report to a Senior Director of AI Foundations
Waymo is an autonomous driving technology company with the mission to be the…
Job responsibilities
- Own the data recipe for Waymo’s Foundation Model pre-training and post-training
- Lead and drive science on best practices around clustering, filtering, de-duplication, long-tail data mining, memorization, etc.
- Tech-lead a team of research engineers to build the data flywheel to enable the above from Waymo’s massive driving data
- Integrate emerging research from the broader community to do rigorous ablations and promising data research techniques.
- Engage with the wider research community on best practices around data centric evaluation creation and refinement
- Partner with engineering and research teams across Waymo to share recipes, techniques, and post-training best practices to accelerate our collective know-how
Job 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 Data-centric AI, 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 and in-depth knowledge of the inner workings of an ML framework (e.g. Pytorch, JAX, Tensorflow)
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