Job description

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. The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Salary Range $251,000 — $310,000 USD

Requirements

Must-haves

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.
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.

Nice-to-haves

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.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mlmachine learning

Platform: greenhouse
Effort: quick