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.

You will:

• Build deep understanding on large-scale sensor data for training and evaluating Foundation Models, with a special focus on scene, mapping, and semantics understanding.
• Create data strategies for Foundation Model Flywheel that advances our critical businesses: large-scale sensor data collection, business-driven sensor data sampling, long-tail data mining, large-scale auto-labeling, targeted efficient human labeling, human-in-the-loop sensor data curation and quality improving.
• Collaborate with foundation model owners to co-evolve our data and model components:  using foundation models to auto-label large-scale driving data, and mining targeted long-tails to improve foundation models.
• Design and develop the large-scale sensor data storage system that enables unified and efficient training, eval, and sensor data mining for foundation models.
• Set up and evolve the working structure between multiple teams across Waymo, where we maximize reuse of infrastructure and tooling.
• Responsible for defining and delivering large-scale and high-quality sensor dataset for foundation model training and evaluation. 

You have:

• Track record in developing reliable and high-quality data infrastructure for large-scale deep learning systems.
• Experience in large-scale data curation, data sampling, data mining, and/or data-model co-evolving.
• Experience in collaborating with horizontal teams, from model teams, infrastructure teams, and data teams, to advance key product success.
• Technical ability to dive deep into complex systems, infrastructure, algorithms, and modeling techniques.

We prefer:

• Having first-hand experience as users of data for large-scale generalist deep learning models.
• Familiarity with the data needs of modern Vision-Language Models (VLMs) or Large-Language Models (LLMs).
• Experience in large-scale distributed systems or products.

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