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 Perception Data team at Waymo is responsible for the overarching strategy and technical steering of all data used to train and evaluate the Waymo Driver’s perception system. We own the end-to-end data lifecycle, building the automated "flywheels" and "infra-as-product" solutions that transform millions of miles of driving sensor data into high-quality training sets. Our work bridges the gap between raw data and advanced machine learning, focusing on complex challenges like active learning loops and open-vocabulary modeling.

By unifying data ingestion, curation, and evaluation into a seamless ecosystem, we enable the rapid development of foundation models and next-generation perception stacks. We collaborate deeply across Machine Learning, Infrastructure, and Evaluation teams to solve "impossible" data problems, ensuring our models can reliably understand the long-tail of rare events. Ultimately, our team provides the essential data foundation that allows the Waymo Driver to navigate the world safely.

In this hybrid role, you will report to a Director of Engineering

You will:

• Define Organizational Technical Strategy: Architect the 2-3 year Data vision for the entire Perception org, unifying the machine learning lifecycle into an automated & continuous flywheel.

• Cross-Organizational Architecture: Drive high-stakes architectural decisions that span across Perception, Machine Learning, and Infrastructure organizations

• Technical Governance & Standards: Establish engineering excellence, API standards, and system reliability bars across the multiple teams under the Director (Data, Eval, Model Lifecycle), ensuring these distinct systems interoperate seamlessly.

• Solve "Impossible" Data Problems: Lead the technical execution on the most ambiguous and complex challenges, such as designing & accelerating active learning loops that automatically curate & learn from rare long-tail events from millions of miles of driving data without human intervention.

• Mentorship at Scale: Serve as a mentor to Staff and Senior engineers across the wider organization, growing the next generation of technical leaders and fostering a culture of rigorous design review.

You have:

• 10+ years of software engineering experience, with at least 5 years in a technical leadership role driving strategy for large-scale distributed systems or ML infrastructure.

• System-of-Systems Architecture: Proven track record of architecting complex, multi-component platforms (e.g., connecting data ingestion, training pipelines, and evaluation loops) that serve 100+ internal engineers or millions of external users.

• Expertise in Big Data & ML Ops: Deep, hands-on mastery of distributed data processing (Spark, Flume, Beam) combined with a strong understanding of ML lifecycles (training, inference, embeddings, fine-tuning).

• C++ & Python Proficiency: Ability to read/write/debug complex C++ and Python code at a system level (e.g., optimizing memory usage in distributed jobs or designing high-performance C++ serving layers).

• Influence Without Authority: Demonstrated ability to align multiple Principals, Directors, and Staff engineers across different organizations (e.g., Infra vs. Product) toward a unified technical direction.

We prefer:

• Foundation Model Infrastructure: Experience building the data infrastructure specifically for training Large Language Models (LLMs) or Vision-Language Models (VLMs) at scale.

• Autonomous Vehicle Domain: Deep familiarity with sensor data (Lidar, Radar, Camera) and the unique challenges of robotics data (calibration, synchronization, latency).

• Active Learning & Data Flywheels: Hands-on experience accelerating & automating the learning process for at-scale ML learning systems.

• Open Source Leadership: Significant contributions to major open-source data or ML projects (e.g., Apache Beam, Tensor Flow, Py Torch, Kubernetes).

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**$281,000—$356,000 USD**