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.

This role follows a hybrid work schedule and you will report to the Vice President, AI Foundations.

You will:

• Be accountable for the productive integration of the Foundation Model Teacher setups developed by the AIF and ML Infra teams into customer student team frameworks, and for maintaining these integrations
• Working with customers to understand their needs and working backwards from those needs
• Optimize the teacher / student model distillation process, model inference footprints and sampling techniques to fit compute constraints
• Define and implement metrics and evaluation infrastructure necessary for evaluating Waymo Foundation Models
• Develop frameworks and approaches that power data ‘Data Flywheel‘ mining and annotation applications using Foundation Models training and evaluation, using Waymo data and also third party data

You have:

• Machine learning expertise, with a track record of contributing to large scale ML production systems and their development cycle
• Software engineering infrastructure experience. Technical ability to dive deep into complex systems, infrastructure, algorithms, and modeling techniques
• Experience with evaluating and optimizing large scale ML / AI systems
• Experience of leading engineering teams of 10+ 
• Hired and developed world class managers and scientists
• Strong communication and planning skills (e.g. building 12 month roadmaps)
• Strong cross-organization collaboration skills

We prefer:

• Direct experience developing large pre-trained foundational models (LLM / VLM / Video models)
• Experience across multiple ML applications (e.g. natural language processing and vision) in multiple settings (e.g. founded a successful startup and lead a significant team at a FAANG company)
• Has experience with the dev-ops side of ML models (e.g. model training latency, regression prevention)