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.

Software Engineering builds the brains of Waymo's fully autonomous driving technology. Our software allows the Waymo Driver to perceive the world around it, make the right decision for every situation, and deliver people safely to their destinations. We think deeply and solve complex technical challenges in areas like robotics, perception, decision-making and deep learning, while collaborating with hardware and systems engineers. If you’re a software engineer or researcher who’s curious and passionate about Level 4 autonomous driving, we'd like to meet you.

The Pin Infrastructure team builds the infrastructure to ensure Waymo customers get picked up and dropped off at the right locations. As we rapidly expand, each new city introduces unique challenges that must be addressed to create a delightful user experience.

You will:

• Collaborate with Machine Learning (ML) teams to integrate new location selection models and phase out legacy heuristic ranking systems to improve pin quality.

• Centralize critical location logic, such as estimated time of arrival (ETA) calculation and venue validation, into a core API to ensure consistency and improve service reasoning.

• Design and implement scalable solutions for managing congestion, particularly for delivery services, to move beyond static location management.

• Develop and deploy geo-fencing and demand control mechanisms to precisely manage Pick-up and Drop-off (PUDO) behavior across new operational areas.

• Build advanced tooling, dashboards, and monitoring systems to debug location selection decisions and proactively identify customer pain points (e.g., long walking distances or high failure rates).

• Refactor and simplify complex backend logic within the core trip planning service to improve maintainability and enable clear explanation of location selection decisions.

You have:

• Proven experience building and scaling high-performance offboard infrastructure for critical services, such as location, routing, or trip planning.

• A strong background in machine learning pipelines, including feature engineering and integrating ML models into high-volume production environments.

• Experience designing and implementing robust, reliable APIs for core geospatial or logistics services.

• Proficiency in data analysis and monitoring to establish system observability and identify anomalies and customer pain points (e.g., through event logging).

• Expertise in developing systems for demand control, traffic shaping, or congestion management within a large-scale service environment.

• Demonstrated background in refactoring large, complex backend services to improve system architecture, maintainability, and diagnostic capabilities.

We prefer:

• Familiarity with geospatial data concepts, including Wayfinding, access point selection, pedestrian path generation, and location semantics.

• Experience with performance tuning, reducing latency, and scaling critical backend systems like a Route Server.

• Background utilizing user-specific signals, preferences, or edit history to personalize selection algorithms.

• Knowledge of advanced location selection concepts, such as enforcing pin diversity and generating multiple viable Pick-up and Drop-off (PUDO) choices.

• Experience with geospatial concepts, including handling venue geometry or multi-level roadgraph support.

• Proficiency with advanced debugging and visualization tools for analyzing location-based events and system behavior.

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**$204,000—$259,000 USD**