The opportunity
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…
What you'll do
Design, implement, and scale controllable generation and test-time compute: algorithms, multimodal conditioning, and critic-guided flow/diffusion steering—for foundation world models.
Investigate multimodal sensor + semantic latent conditioning, and: representation alignment to advance toward holistic world simulation.
Leverage learned realism critics and agentic autoresearch workflows on: large-scale TPU clusters to evaluate closed-loop interactive realism, long-tail scenario synthesis, and multimodal consistency.
Write clean, modular, and well-tested JAX code in core simulation and: world-modeling codebases, collaborating closely with researchers across Sim Realism, Foundation Models, and Closed-Loop Training teams.
Currently pursuing a PhD in Computer Science, Machine Learning, Robotics, or: a related technical field, with an anticipated graduation date between December 2027 and August 2028 (returning to a degree program for at least one academic term after the internship).
Core Implementation Fluency (Mandatory): Strong tensor implementation and debugging skills in Python and JAX/Flax or PyTorch, with hands-on experience training and modifying modern neural architectures (such as Transformers, Diffusion/Flow-Matching models, or deep RL/critic networks).
What they're looking for
- Deep Research Expertise in Either: (a) Controllable Generative World / Sequence Modeling & Inference-Time Scaling (hands-on research experience in continuous flow matching / Rectified Flows, score-based diffusion guidance/steering, test-time compute / SMC, or parameter-efficient finetuning) OR (b) Multimodal Spatiotemporal Representation Learning & Closed-Loop Simulation (hands-on research experience in 4D/video tokenization, latent dynamics alignment such as REPA/DINO alignment, inverse RL / adversarial discriminators, or multi-agent trajectory modeling), with working familiarity in the other.
- First-author publications at top-tier machine learning, computer vision, or: robotics venues (e.g., NeurIPS, ICML, ICLR, CVPR, ECCV, CoRL, RSS, ICRA, or equivalent venues) in generative world models, flow/diffusion steering, controllable generation, or autonomous driving simulation.
- Experience with multi-agent closed-loop simulation,: counterfactual/adversarial traffic generation, or Sim2Real discriminator/critic stabilization (e.g., Waymax, WOMD, NuPlan, MGAIL, or reward-hacking mitigation).
- Experience profiling and scaling distributed training and inference workloads: on TPUs or multi-node GPUs (such as XLA compilation, vectorized batched rollouts, and memory optimization) and interest in external conference publication on Waymax/WOMD.
