About the job
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.
Responsibilities
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
Qualifications
Minimum
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
Preferred
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)