Description
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 Staff Software Engineer.
You will:
- Partner with foundation model training teams to determine optimal data mixtures, training curricula, objectives, and other design choices that shape model capabilities.
- Design global-scale pipelines for dynamically updating and reusing data mixtures as our understanding of model behavior evolves and real-world datasets are continually ingested.
- Develop and deploy principled data-selection algorithms (e.g. influence estimation, proxy models, gradient similarity search) to find targeted subsets of data for fine tuning and RL.
- Run large-scale empirical studies linking pre-training data composition to downstream post-training performance, translating those scaling laws into optimized production training runs.
- Collaborate closely with behavior, perception, and evaluation teams to test these foundation models and measure their impact on the performance of the Waymo Driver.