2026 Intern, PhD, Machine Learning Engineer, Simulation

The Waymo Simulation team builds state-of-the-art simulations of realistic environments for testing and training the Waymo Driver, which is Waymo's autonomous driving technology. They develop industry-leading simulation solutions using advanced machine learning algorithms to measure and enhance the performance of the Waymo Driver. Waymo interns work with leaders in the industry on projects that deliver significant impact to the company. We believe learning is a two-way street: applying your knowledge while providing you with opportunities to expand your skillset. Interns are an important part of our culture and our recruiting pipeline. Join us at Waymo for a fun and rewarding internship!

This internship will be based on-site at our headquarters in Mountain View, CA.

• Research, implement, and evaluate state-of-the-art generative models and advanced sampling techniques for ultra-realistic multi-agent simulations and full fidelity scenario generation.
• Collaborate with research and engineering teams to integrate models into Waymo's simulation and verification workflows, applying cutting-edge VL and ML efficiency technology.
• Contribute to projects that evaluate and improve world modeling of high fidelity sensor data for Waymo's E2E AI-first Driver, with opportunities to publish novel research.

• Currently pursuing a Ph.D. in Computer Science, Machine Learning, Electrical Engineering, or a related technical discipline.
• Strong publication record in top-tier AI/ML or VL or CV conferences and demonstrated expertise in AI Architecture, Deep Learning, Computer Vision Reinforcement Learning, High Performance Computing, Compilers, and/or Computer Architecture.
• Hands-on experience with deep learning frameworks (e.g., PyTorch, TensorFlow, JAX) and ML coding, proficiency in Python and/or C/C++

• Experience with large-scale model training, including large diffusion models, world modeling, foundational models, ML modeling and system design and experience in 3D Vision, including 3D or 4D Gaussian Splatting, 3D Assets Generation, etc.
• Proficiency in ML frameworks like Jax, XLA, and/or TPU, and contributions to relevant open-source projects.
• Demonstrated expertise in research domains such as Deep Learning, Reinforcement Learning, Distributed systems, AI Architecture, and familiarity with 3DGS/NeRF technologies.