Senior Software Engineer, Simulation ML Infrastructure

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. The Simulation ML Infrastructure team builds scalable AI/ML infrastructure to accelerate the Simulator team in sustainably innovating and building state of the art simulations of realistic environments for the testing and training of the Waymo Driver. To increase the fidelity and steerability of the simulations, we employ large foundation models trained on massive datasets to model the real world, including but not limited to, realistic agents (vehicles, pedestrians, cyclists, motorcyclists etc.), roads, traffic control systems, and weather etc. We seek an experienced senior IC to lead the development of advanced AI/ML infrastructure for multi-billion parameter foundation models in ML accelerator-friendly simulations. Your expertise in massive model scaling, ML accelerators, and large-scale distributed systems will be required for designing and scaling our systems. This role reports to an Engineering Manager. You will: Be part of a world-class, high-performing research engineering team to advance the state of the art of ultra realistic multi-agent simulations using foundation models. Collaborate closely with the core Waymo Realism Modeling team in London and Waymo Oxford to use large foundation models to improve sim realism. Work at the intersection of data engineering, model development, and simulations, and drive architectural decisions. Own large, complex systems, driving architectures and designs that meet technical and business objectives. Design and scale large distributed systems covering the ML lifecycle, supporting planet-scale dataset generation, model training, and evaluation. Collaborate cross-functionally to derive performance and system-level requirements for large ML systems. Translate product/business goals into measurable technical deliverables, ensuring system component alignment. We prefer: 5+ years of professional software engineering experience, with at least 3 years in machine learning infrastructure such as developing, designing, scaling, training, deploying, and optimizing large-scale machine learning systems from data to model. Solid experience in the development and optimization of machine learning infrastructure tools like DeepSpeed, PyTorch, TensorFlow, Ray, or similar frameworks. Strong understanding of state-of-the-art machine learning models and algorithms such as autoregressive transformers and familiarity scaling large models across ML accelerator profiling tools to uncover performance bottlenecks. Strong leadership skills with experience driving ambiguous problems end-to-end, with a willingness and independence to pick up whatever knowledge to get the job done. Passionate about building infrastructure, libraries, tools, and pipelines for engineers and scientists. Excellent communication skills, both verbal and written, with the ability to translate complex technical concepts for a broad audience. Practical familiarity in Autonomous Driving, Simulations, and ML accelerators is a plus. 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 locati