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2027 Summer Intern, PhD, Machine Learning
Waymo
Mountain View, California, United StatesFresher11m agovia GreenhouseH-1B sponsor · 6 entry-level filings
This job at a glance
- Who can apply
- New grads and up to 1 year
- Must know
- Data structures & algorithmsPythonPyTorch
- Nice to have
- Machine learningComputer visionNode.js
- Work
- On-site in Mountain View · internship
- Pay
- Not stated by the employer.
- Skill demand
- Python17%Machine learning8%Data structures & algorithms2%PyTorch1%of 100 similar entry-level jobs posted in the US this week
- Listed
- Nov 6, 2026 · still open
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About the Role
Waymo is an autonomous driving technology company focused on building the Waymo Driver to improve mobility and save lives. The Software Engineering team builds the core technology that allows the Waymo Driver to perceive, decide, and navigate safely. This internship offers an opportunity to partner with industry leaders on impactful projects.
Key Responsibilities
- 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.
Requirements & Tech Stack
- 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.
- Core Implementation Fluency: 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).
- (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).
- Working familiarity with the other area of expertise listed above.
Preferred Qualifications
- 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) 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.
Internship Type: Hybrid onsite.
Hourly Rate: $85 USD.
Interns are eligible for the Company’s generous benefits programs, subject to eligibility requirements.