2027 Summer Intern, PhD, Machine Learning - Simulation & Closed-Loop Training
Waymo · Mountain View, California, United States
About this role
**Waymo — 2027 Summer Intern (PhD) | Machine Learning — Simulation & Closed-Loop Training** Waymo is an autonomous driving technology company with the mission to be the world’s most trusted driver. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and is supported by extensive real-world driving experience and large-scale simulation. **Software Engineering** builds the brains of Waymo’s fully autonomous driving technology—enabling perception, decision-making, and safe delivery to destinations. --- ## What you will do - Design, implement, and scale Reinforcement Learning post-training and distillation algorithms (e.g., flow/diffusion policy optimization, off-policy importance sampling, 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 behaviors with human driving realism and safety. - Run rigorous closed-loop ablation studies on large-scale TPU clusters, using agentic auto-research workflows and fleet-scale driving behavior metrics (e.g., route progress, braking comfort, lateral stability, 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. --- ## What 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** (and 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 models, deep RL networks). - Deep research expertise in **either**: - **(a) Reinforcement Learning & Post-Training** (e.g., PPO/GRPO/REINFORCE, off-policy/offline RL like importance sampling/AWR/DPO, constrained MDPs, inverse RL, learned reward/critic modeling), **or** - **(b) Generative World / Sequence / Trajectory Modeling** (e.g., continuous flow matching / Rectified Flows, score-based diffusion policies like DDPO/DPPO/Diffuser, spatiotemporal autoregressive world/trajectory models), with working familiarity in the other. --- ## We prefer - First-author publications at top-tier ML/robotics venues (e.g., NeurIPS, ICML, ICLR, CVPR, CoRL, RSS, ICRA) 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, discriminator stabilization). - Experience profiling and scaling distributed training workloads on TPUs or multi-node GPUs (e.g., XLA compilation, vectorized batched rollouts, memory optimization) and familiarity with C++/Python interfaces. --- ## Notes - **Hybrid onsite internship**. - Resumes accepted on a **rolling basis** until the role is filled. - If you want to be considered for multiple roles, you must apply to each one individually (apply to the **top 3 roles** you’re interested in). --- ## Pa
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