2027 Summer Intern, PhD, Research, Behavior Post-training

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.

Waymo interns partner with leaders in the industry on projects that create impact to the company. We believe learning is a two-way street: applying your knowledge while providing you with opportunities to expand your skill-set. Interns are an important part of our culture and our recruiting pipeline. Join us at Waymo for a fun and rewarding internship!

You will:

• Build tools for sampling, selecting, and discarding synthetic driving data with testable hypotheses of how that data will lead to improvement
• Train and evaluate models with mixtures of synthetic and logged data
• Present and write about methods for synthetic data generation and training

You have:

• Currently pursuing a PhD in Computer Science, Machine Learning, Robotics, Electrical Engineering or a related technical field
• Familiarity with methods of sampling from large models
• Experience with post-training methods such as SFT, RL, and preferences
• Modern deep learning frameworks such as PyTorch and Jax

We prefer:

• Experience working with open and closed loop decision making models in robotics
• Experience with synthetic data generation and evaluation
• Prior projects using LLM-as-a-judge

Note: This will be a hybrid onsite internship position. We will accept resumes on a rolling basis until the role is filled. To be in consideration for multiple roles, you will need to apply to each one individually - please apply to the top 3 roles you are interested in.