Software Engineer, Pin Infra

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.

In this hybrid role, you will report to an Engineering Manager.

You will:

• Collaborate with Machine Learning (ML) teams to integrate new location selection models to improve pin quality.
• Centralize critical location logic to ensure consistency and improve service reasoning.•Design and implement scalable solutions for managing congestion.
• Develop and deploy 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 and proactively identify customer pain points.

You have:

• Experience building and scaling high-performance infrastructure for critical services.
• 3+ years of experience.
• C++ (or another OOP language) proficiency

We prefer:

• Prior experience working with autonomous driving vehicles.Experience designing and implementing robust, reliable APIs for core geospatial or logistics services.Experience with performance tuning, reducing latency, and scaling critical backend systems.Proficiency with advanced debugging and visualization tools for analyzing location-based events and system behavior.
• Demonstrated background in refactoring large, complex backend services to improve system architecture, maintainability, and diagnostic capabilities.
• Expertise in developing systems for demand control, traffic shaping, or congestion management within a large-scale service environment.
• A background in machine learning pipelines, including feature engineering and integrating ML models into high-volume production environments.

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