The opportunity
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…
What you'll do
Scalable ML Architecture & Signal Expansion: Architect a modular, extensible ML framework capable of rapidly absorbing a growing list of complex signal requirements. You will lead the transition from specialized models to unified Foundation Model architectures that maintain "Oracle" quality while scaling to hundreds of new semantic categories.
Strategic Ownership & Quality Leadership: Define the multi-year roadmap for signal quality, coverage, and reliability. Your mandate is to ensure our signals don’t just match onboard software—they set the industry benchmark for ground truth accuracy.
Engineering at Extreme Scale: Oversee a pipeline processing millions of segments with a requirement for >99% cache hit rate. You will lead the team in optimizing high-fidelity ML inference costs across massive, petabyte-scale datasets to support rigorous, high-frequency evaluation.
Stakeholder & Ecosystem Growth: Act as the primary interface for "Application Owners" across the company. You will translate complex simulation needs into technical requirements and aggressively grow our customer base to ensure every major software launch is "Oracle-validated."
Operational Excellence: Lead a high-performing team of ML and Backend Engineers, fostering a culture of technical excellence while delivering on mission-critical P0 initiatives.
Education: Master’s or PhD in Computer Science, Machine Learning, Robotics,: or a related field (PhD preferred).
What they're looking for
- Management Experience: Experience managing and technically leading a team of 10+ Engineers or Research Scientists.
- Cross-Stakeholder Alignment: Proven track record of navigating complex organizations to gather requirements from diverse customers and aligning them into a unified technical roadmap. You can negotiate Roles & Responsibilities (R&R) across competing priorities.
- Technical Depth: Hands-on experience with an ability to technically lead ICs daily, including deep knowledge of codebases, eval/data pipelines, and model architectures.
- ML Expertise: Deep understanding of Foundation Models (LLMs, VLMs, or MMLMs), specifically in the context of post-training, fine-tuning, or inference-time scaling (e.g., MCTS, Chain-of-Thought).