Waymo
2027 Summer Intern, PhD, Planner Machine Learning
San Francisco, California +1 · Internship · Posted Sep 29, 2026
- Location
- San Francisco, California
San Francisco, California, United States - Education
- Currently pursuing a Ph.D. in Machine Learning or a related quantitative field.
About the job
Researches and builds machine-learning models for Waymo’s Large Selection model, evaluating their performance in simulated and real-world driving scenarios.
What you'll do
- Design and implement deep-learning architectures, multi-task losses, and preference or ranking objectives using Python and Jax.
- Train and diagnose neural networks on distributed TPU infrastructure.
- Evaluate models on open-loop and closed-loop benchmarks and analyze real-world driving scenarios.
What they ask for
- Currently pursuing a Ph.D. in Machine Learning or a related quantitative field.
- Strong hands-on proficiency in Python and at least one modern deep-learning framework, such as JAX or 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.
Nice to have
- First-author publications at top-tier ML, robotics, or vision venues; experience with transformers, preference alignment, or sequential decision-making.
- Familiarity with autonomous driving and motion planning; experience with C++ or large-scale production ML codebases and distributed data pipelines.
Pay
$85 USD per hour (hourly PhD pay)
Tools
Python, Jax, TPU, JAX, PyTorch, C++
AI focus
Develops and evaluates deep-learning models, training objectives, and preference or ranking methods for autonomous-driving planning.
Model-written summary · the original posting is authoritative