7 hours ago
Responsibilities
- Research, design, and implement novel deep learning architectures, multi-task loss formulations, and preference or ranking objectives in Python and JAX.
- Train and diagnose large-scale neural networks using distributed TPU infrastructure.
- Analyze training dynamics, gradient conditioning, and representation quality.
- Evaluate trained models across open-loop and closed-loop benchmarks.
- Triage real-world driving scenarios to connect modeling choices with physical vehicle behavior.
Requirements
- 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.
- Preferred: first-author publications at top-tier ML, robotics, or vision venues such as NeurIPS, ICML, ICLR, CoRL, ICRA, or CVPR.
- Preferred: experience with transformer architectures, post-training or preference alignment, sequential decision making, autonomous driving, motion planning, C++, large-scale production ML codebases, or distributed data pipelines.
Benefits
- Hybrid onsite internship position.
- Full-time summer internship with a stated hourly PhD pay rate of $85.
- Interns are eligible for Waymo’s benefits programs, subject to eligibility requirements.
- Applications are accepted on a rolling basis until the role is filled.
Categories
About Waymo
Waymo develops the Waymo Driver, a full-stack autonomous driving system, and operates the Waymo One robotaxi service that offers paid, driverless rides in select U.S. cities. The company monetizes through ride-hailing fares and partnerships integrating its technology on multiple vehicle platforms. Founded in 2009 as Google’s self-driving project, Waymo is headquartered in Mountain View and is an Alphabet subsidiary, with over 10 million rider-only trips and 100+ million autonomous miles on public roads.