2027 Summer Intern, PhD, Planner Machine Learning

Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. Software Engineering builds the brains of Waymo's fully autonomous driving technology. Our software allows the Waymo Driver to perceive the world around it, make the right decision for every situation, and deliver people safely to their destinations. We think deeply and solve complex technical challenges in areas like robotics, perception, decision-making and deep learning, while collaborating with hardware and systems engineers. If you’re a software engineer or researcher who’s curious and passionate about Level 4 autonomous driving, we'd like to meet you. 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: Research, design, and implement novel deep learning architecture, multi-task loss formulations, and preference/ranking objectives in Python and Jax for Waymo’s Large Selection model. Train and diagnose large-scale neural network using distributed TPU infrastructure, analyzing training dynamics, gradient conditioning and representation quality. Evaluate trained models across open-loop and closed-loop benchmarks, triaging real-world driving scenarios to connect mathematical modeling choices with physical vehicle behavior. You have: Currently pursuing a Ph.D. in Machine Learning or a related quantitative field. Strong hands-on proficiency in Python with at least one modern deep learning framework (e.g., JAX, PyTorch). Solid mathematical foundation in deep learning, optimization, loss formulation and empirical model diagnostics. Experience designing, running, and analyzing rigorous ML experiments on large-scale datasets. We prefer: Track record of first-author publications at top-tier ML, robotics, or vision venues (e.g., NeurIPS, ICML, ICLR, CoRL, ICRA, CVPR, etc). Experience with transformer architectures, post-training/preference alignment, or sequential decision making. Familiarity with autonomous driving and motion planning. Experience with C++ or navigating large-scale production ML cobebases and distributed data pipelines. 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. The expected hourly rate for this full-time position is listed below. Interns are also eligible to participate in the Company’s generous benefits programs, subject to eligibility requirements. Hourly PhD Pay $85—$85 USD