Director, Foundation Model Data Recipes

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 the Senior Director, AI Foundations.

You will:

• Participate in Waymo’s Foundation World Model pre-training and post-training by owning the data recipe
• Invest in data best practices - best practices around clustering, filtering, de-duplication, long-tail data mining, memorization, etc. Also help 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. Focus on managing a team of scientists that would work on scaling ladders for pre-training, data mix optimizations and RL recipes (preferences) for post-training. 
• 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

You have:

• PhD or Masters in Computer Science, Machine Learning, Robotics, or a similar technical field; with 5+ 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
• Experience leading teams of research scientists and engineers to deploy real world impact in AI - including showing real world impact. Ability to be hands-on when needed to adapt models as and when needed.

We prefer:

• Extensive experience working with data and recipes for large scale foundation models
• PhD in Computer Science, Machine Learning, or Robotics, with a research focus on Data centric AI, Reinforcement Learning, Foundation Models, or Multimodal learning