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
