Staff Machine Learning Infrastructure Engineer, Simulation

The Simulation Machine Learning 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 realistic agents (vehicles, pedestrians, cyclists, motorcyclists), roads, traffic control systems, and weather

We are looking for an experienced Staff Machine Learning Infrastructure Engineer 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 distributed training will be required for designing and scaling our systems.

This role reports to an Engineering Manager.

In this role, you’ll:

• Be part of a world-class, research engineering team to improve the of ultra realistic multi-agent simulations using foundation models.
• Collaborate with the core Waymo Realism Modeling team in London and Waymo Oxford to use large foundation models to improve sim realism.
• Provide deep technical leadership on large-scale ML model architectures, especially for autonomous vehicle models. Work at the intersection of data engineering, model development, and simulations, and provide guidance on architectural decisions and technical directions. Manage complex systems, driving architectures that meet technical and goals.
• Design and scale large distributed systems covering the ML lifecycle, supporting planet-scale dataset generation, model training, and evaluation.
• Collaborate to derive performance and system-level requirements for large ML systems. Translate product/business goals into measurable technical deliverables, ensuring system component agreement.
• Mentor junior engineers, growing their expertise and promoting a collaborative culture.

Preferred qualifications

• 8+ years of professional software engineering experience, with at least 5 years in machine learning infrastructure such as developing, 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.
• Expertise in distributed training techniques, including gradient sharding and optimization strategies for scaling large models across ML accelerator profiling tools to uncover performance bottlenecks. Familiarity with custom kernels for compute based efficiency.
• Experience with state-of-the-art machine learning models such as autoregressive transformers. 
• With experience navigating cross-functional teams and providing technical leadership projects across multiple organizations.
• , with the ability to translate complex technical concepts for a broad audience.
• Practical familiarity in Autonomous Driving, Simulations, and ML accelerators is a plus.