2027 Summer Intern, PhD, Machine Learning - Generative World Models & Controllable Simulation
Waymo · Mountain View, California, United States
About this role
**Waymo — 2027 Summer Intern (PhD) | Machine Learning — Generative World Models & Controllable Simulation** Waymo is an autonomous driving technology company on a mission to be the world’s most trusted driver. Software Engineering builds the brains of the Waymo Driver—enabling perception, decision-making, and safe delivery to destinations. **You will:** - 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. **You have:** - Currently pursuing a PhD in Computer Science, Machine Learning, Robotics, or a related technical field, with an anticipated graduation date between **Dec 2027 and Aug 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 (e.g., Transformers, Diffusion/Flow-Matching models, or deep RL/critic networks). - Deep research expertise in either: - (a) **Controllable Generative World / Sequence Modeling & Inference-Time Scaling** (e.g., continuous flow matching / Rectified Flows, score-based diffusion guidance/steering, test-time compute / SMC, or parameter-efficient fine-tuning), **or** - (b) **Multimodal Spatiotemporal Representation Learning & Closed-Loop Simulation** (e.g., 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. **We prefer:** - First-author publications at top-tier ML/CV/robotics venues (e.g., NeurIPS, ICML, ICLR, CVPR, ECCV, CoRL, RSS, ICRA) 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/inference on **TPUs or multi-node GPUs** (e.g., XLA compilation, vectorized batched rollouts, memory optimization) and interest in publishing externally on Waymax/WOMD. **Notes:** - This is a **hybrid onsite** internship. - Resumes are accepted on a **rolling basis** until the role is filled. - To be considered for multiple roles, apply to each one individually—please apply to the **top 3 roles** you’re interested in. **Pay (Hourly PhD Pay):** **$85–$85 USD/hour**
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