Currently pursuing a PhD in Computer Science, Machine Learning, Robotics, or a related field, graduating between December 2027 and August 2028.
Strong tensor implementation and debugging skills in Python and JAX/Flax or PyTorch.
Hands-on experience with modern neural architectures like Transformers and deep RL networks.
Deep research expertise in either controllable generative modeling or multimodal representation learning.
First-author publications in top-tier machine learning or robotics venues are preferred.
Responsibilities
Design and implement algorithms for controllable generation and test-time compute.
Investigate multimodal sensor conditioning and representation alignment.
Leverage realism critics on TPU clusters for evaluating interactive realism.
Write clean, modular JAX code for simulation and world modeling.
Collaborate with researchers across various teams on core projects.
Benefits
Participation in the company's generous benefits programs, subject to eligibility requirements.
Hybrid onsite internship opportunity.
Full Job Description
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.
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
About Waymo
Waymo LLC is a self-driving technology development company. It is a subsidiary of Alphabet Inc., the parent company of Google. Waymo develops autonomous driving technology and provides ride-hailing services through its Waymo One program. The company has been testing its self-driving technology on public roads since 2009 and has logged millions of miles of autonomous driving. Waymo has partnerships with several automakers and has been working on developing autonomous trucks for use in logistics.
