Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.
Join our ML Infrastructure engineering team advancing state-of-the-art ultra-realistic multi-agent simulations using foundation models. In this role, you will work at the intersection of ML infrastructure, foundation models, and simulation engineering, with a specific focus on writing high-performance business and simulation logic in JAX/TensorFlow running directly on TPUs to power realistic environments for Reinforcement Learning (RL).
What You'll Do
- Design, build, and optimize realistic simulation environments and business logic running on TPUs using JAX and TensorFlow. Implement and optimize large-scale model and data parallelism strategies for training and running foundation models on TPU hardware.
- Collaborate closely with modeling teams to integrate foundation models into simulation pipelines.
- Drive technical architectures and system designs from data engineering through simulation execution to meet business and performance objectives.
- Profile systems, identify performance bottlenecks across ML accelerators, and optimize end-to-end execution speed.
- Translate product and business goals into concrete technical requirements and system deliverables.
Minimum Qualifications
- 6+ years of professional software engineering experience, with at least 4 years focused on machine learning infrastructure (scaling, training, optimizing, and deploying large-scale ML systems).
- Direct ML programming experience on TPU and GPU hardware using frameworks such as JAX, PyTorch, or TensorFlow.
- Proven hands-on experience scaling large models using model parallelism, data parallelism, or distributed training techniques.
- Strong understanding of state-of-the-art ML models (e.g., autoregressive transformers) and hands-on proficiency with ML accelerator profiling tools to diagnose bottlenecks.
- Demonstrated ability to independently lead ambiguous technical initiatives end-to-end and build robust libraries, pipelines, and developer tooling.
- Strong verbal and written communication skills to collaborate effectively across distributed, cross-functional teams.
Preferred Qualifications
- Practical experience in Reinforcement Learning (RL), Sim2Real transfer, or Robotics.
- Experience with distributed ML frameworks and accelerators like GPU/TPU.
- Domain familiarity with Autonomous Driving systems, multi-agent simulations, or realistic world modeling.
