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.

Within Perception, the Perception Data team builds and operates the data infrastructure that powers Waymo's perception foundation models. We design scalable pipelines that process petabytes of sensor data and allow everything from rare event mining to large-scale offline model deployment. Our work directly supports model training, evaluation, and production, ensuring that perception models have the high-quality, diverse data they need to perform safely. We collaborate across ML, Simulation, and Infrastructure teams to integrate data into the model lifecycle and help bring new capabilities to the Waymo Driver.

In this hybrid role, you will report to an engineering manager.

You will:

• Define the technical strategy and long-term architecture for petabyte-scale data pipelines that power Waymo’s perception foundation models.

• Drive cross-functional initiatives across ML, Simulation, and Infrastructure teams to solve complex data ambiguity and integrate data into the model lifecycle.

• Architect advanced systems for data mining, curation, and active learning using ML techniques (e.g., embeddings, vector search, offboard inference).

• Serve as a technical lead and mentor for senior engineers, setting engineering standards and conducting code/design reviews for C++ and Python systems.

• Lead the design evolution of critical data infrastructure to ensure high scalability and reliability for offline model deployment and rare event mining.

• Accelerate model deployment velocity across new cities and vehicle platforms by optimizing end-to-end data lifecycles.

You have:

• C++ programming skills (required), with Python experience.

• 8+ years of experience with large-scale distributed data systems (e.g., Spark, Beam, Dataflow), or PhD and 5+ years of relevant experience.

• Proven track record of architecting and delivering complex, multi-system ML data engineering or active learning solutions.

• Experience providing technical leadership, managing project priorities, and influencing technical strategy across multiple teams.

• Deep understanding of end-to-end ML data pipelines and their interaction with model training and evaluation.

We prefer:

• Experience designing vector search (Faiss, ScaNN) or RAG systems at scale.

• Deep knowledge of ML infrastructure for foundation model training, fine-tuning, and evaluation.

• Expertise in data-centric AI methodologies (few-shot, pre-training) and their application in autonomous systems.

• Background in autonomous driving, robotics, or similar high-complexity hardware/software environments.

• MS/PhD or published work in Machine Learning, large-scale data systems, or related fields.

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**$238,000—$302,000 USD**