2027 Summer Intern, PhD, Machine Learning

About the Role

Waymo is seeking a 2027 Summer Intern for PhD-level research in Machine Learning, focusing on Simulation & Closed-Loop Training. This role is part of the Software Engineering team, which builds the core autonomous driving technology.

Key Responsibilities

• Design, implement, and scale Reinforcement Learning post-training and distillation algorithms (such as flow or diffusion policy optimization, off-policy importance sampling, and constrained multi-objective RL) for foundation world models in simulation and autonomous driving.
• Develop learned reward models, realism critics, and discriminators within a closed-loop multi-agent simulator to align simulated agent and driving behaviors with human driving realism and safety.
• Run rigorous closed-loop ablation studies on large-scale TPU clusters—leveraging agentic auto-research workflows and fleet-scale driving behavior metrics (such as route progress, braking comfort, lateral stability, and lane selection)—to mitigate simulator exploitation and distribution shift.
• Write clean, modular, and well-tested JAX code in core simulation and autonomous driving codebases, collaborating closely with researchers and engineers across simulation, world modeling, and autonomous driving teams.

Requirements & Tech Stack

• Currently pursuing a PhD in Computer Science, Machine Learning, Robotics, or a related technical field.
• 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: 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 models, or deep RL networks).
• Deep Research Expertise in either:
• Reinforcement Learning & Post-Training: Hands-on research experience in policy gradient methods (e.g., PPO/GRPO/REINFORCE), off-policy or offline RL (e.g., importance sampling/AWR/DPO), constrained MDPs, inverse RL, or learned reward and critic modeling.
• Generative World, Sequence, or Trajectory Modeling: Hands-on research experience in continuous flow matching / Rectified Flows, score-based diffusion policies (e.g., DDPO/DPPO/Diffuser), or spatiotemporal autoregressive world and trajectory models.
• Working familiarity with the other area of deep research expertise.

Preferred Qualifications

• First-author publications at top-tier machine learning or robotics venues (e.g., NeurIPS, ICML, ICLR, CVPR, CoRL, RSS, ICRA, or equivalent venues) in RL post-training, flow/diffusion policy optimization, generative world models, or autonomous driving.
• Experience with multi-agent closed-loop simulation, Sim2Real transfer, or reward-hacking mitigation (e.g., Waymax, WOMD, NuPlan, IsaacGym/MuJoCo, adversarial traffic generation, or discriminator stabilization).
• Experience profiling and scaling distributed training workloads on TPUs or multi-node GPUs (such as XLA compilation, vectorized batched rollouts, and memory optimization) and familiarity with C++/Python interfaces.

This will be a hybrid onsite internship position.

We will accept resumes on a rolling basis until the role is filled.

The expected hourly rate for this full-time position is $85 USD.

Interns are eligible to participate in the Company’s generous benefits programs, subject to eligibility requirements.