Staff Research Scientist, Foundation Models 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 a Senior Director of AI Foundations

You will:

• 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. This includes 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

• 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)

Nice to have

• Extensive experience working with data and recipes for large scale foundation models
• ML infra experience: training, evaluating and deploying ML models at scale
• Deep learning experience, especially with generative models, e.g., LLMs/VLMs, and/or reinforcement learning

• 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