Software Development Engineer II, Geospatial

Are you fascinated with the idea of creating a digital representation of the world? What about building learning systems to create the most precise and accurate representation? Join the Geospatial team to build Maps for Amazon.

We build learning systems to identify road networks, POIs, and geocodes of addresses worldwide. Our delivery operations use these systems to determine the locations and plan the routes for delivery. Drivers use these systems to navigate to the delivery locations. Our mission is to vend geospatial data (e.g. maps, traffic, addresses, and locations/geocodes) that is detailed and fresh through an intuitive experience that enables every driver – independent of tenure and affinity – to succeed in their delivery tasks.

We are not creating another consumer-grade mapping solution, we are building systems that enable depth focused solutions. For example, we are interested in not only getting a person to an address like 300 Boren Ave N, we are also interested in guiding them where to park, where the building entrance, find out if there is a mailing room in the building that is open at that time, and guide them to alternative delivery location at the customer unit based on various factors. The level of detail we provide on the delivery journey is leading in the mapping industry. Our geocodes also dictate how to optimally group the packages for that stop based on proximity and walk times, which significantly impacts route plan and stop plan efficiencies. Several of these problems require us to build systems that can work with an ensemble of ML models as well as support the right segmentation of inputs to make good estimates on the outputs.

There are several unsolved or partially solved problems in this space; such as parsing and managing both structured and unstructured addresses in various countries, modeling building entrances and unit access in multi-unit buildings, optimal polygonal geofence guardrails, and multiple delivery locations with business and transportation mode awareness.

The technical domain is multi-faceted. We build low-latency, highly-available services, we build big data processing pipelines to refresh or produce new data, we train ML models, and we run science experiments. We also build solutions for these capabilities, ranging from state-of-the-art ML platforms (including generative AI) to modeling, storing, vending, and managing large amounts of data.

Our key output metrics include location accuracy, coverage, and predictive accuracy of service and transit estimates. We also measure the operational impact of these inputs on delivery success and transporter experience.

If you have an entrepreneurial spirit, know how to deliver, are deeply technical, highly innovative and long for the opportunity to build pioneering solutions to challenging problems, we want to talk to you.

Key job responsibilities
- Participate in the design, implementation, and deployment of successful large-scale systems and services in support of our transportation operations and the businesses they support.
- Participate in the definition of secure, scalable, and low-latency services and efficient physical processes.
- Work in expert cross-functional teams delivering on demanding projects.
- Functionally decompose complex problems into simple, straight-forward solutions.
- Understand system inter-dependencies and limitations.
- Share knowledge in performance, scalability, enterprise system architecture, and engineering best practices.

A day in the life
- Collaborate with engineering and science teams on building state-of-the-art scalable, big data pipelines, ML training and inference solutions, high-availability services with low latencies.
- Analyze nuanced metrics to understand system behavior and innovate on solutions to optimize our success metrics.
- Play a key role in the technology that empowers the delivery of millions of smiles every day around the world.

About the team
The broader Geospatial organization covers Amazon Maps, driving and walking time estimation and the transporter navigation experience. This team focuses on destination related data and locations for parking, building entries, delivery locations and other waypoints throughout the transporter delivery journey, and how they relate to places, floors and building layouts. We build systems that learn, ingest, rank and vend data related to locations.