Director, Foundation Model Data Recipes

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 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.

Responsibilities

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; 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

Requirements

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

Nice to have

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

Benefits

Discretionary annual bonus program; equity incentive plan; Company benefits program; top-notch medical, dental and vision insurance; mental wellness support; gym membership; wellness programs; competitive compensation; bonus opportunities; employee discounts; flexibility to work from another location for four weeks per year; on-site or hybrid work model; remote working opportunities; paid time off; bereavement leave; sick leave; parental leave