Machine Learning Intern - Generative World Models & Controllable Simulation

Software Engineering builds the brains of Waymo's fully autonomous driving technology. Our software allows the Waymo Driver to perceive the world around it, make the right decision for every situation, and deliver people safely to their destinations. We think deeply and solve complex technical challenges in areas like robotics, perception, decision-making and deep learning, while collaborating with hardware and systems engineers. If you’re a software engineer or researcher who’s curious and passionate about Level 4 autonomous driving, we'd like to meet you. 

Waymo interns partner with leaders in the industry on projects that create impact to the company. We believe learning is a two-way street: applying your knowledge while providing you with opportunities to expand your skill-set. Interns are an important part of our culture and our recruiting pipeline. Join us at Waymo for a fun and rewarding internship!

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 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).
• 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.

We prefer:

• 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.

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.

Hourly PhD Pay 85 - 85 USD