Maps Intern

• In this role you will contribute to, build, and own portions of Zipline’s Maps stack. That includes contributing to an extensible path planning service used for generating routes that are operationally flown every day. It also includes a suite of services for data generation, transformation and access for path planning and flight data analytics
• Your work within the team could range from path planning algorithms, validation of artifacts for compliance with flight requirements, to spatial data generation and data transformation pipelines. Your work will help us achieve multi-objective routing and efficient spatial data ingestion and access, enabling Zipline to serve more customers around the world. From this internship, you can expect to learn about how theoretical path planning algorithms are adapted for a real world problem, and how data and spatial search interact to enable routing in air
• Work on services that generate routes and spatial planning artifacts
• Use tools to visualize and evaluate geospatial features that impact serviceability
• Work with teams to make spatial data accessible instantaneously anywhere in the world
• Improve planning algorithms to make routing fast, safe, and reliable
• Examples of Previous Internship Projects:
• Prototyping a new path planning algorithm to enhance long-range routes
• Incorporating a multidimensional risk data into the routing engine
• Creating tooling for generating composite Digital Elevation Models
• Building infrastructure to enable our zips to survey large areas
• This internship is a full-time position, in-person at our South San Francisco office. We will host our Spring 2027 interns from (January 2027 - April 2027)

Our ideal candidate works well in startup environments, is comfortable wearing many hats, and is excited to learn about many aspects of how Zipline’s spatial planning works today

Must have at least completed the second year of your Undergraduate studies. Masters and PhD students are also eligible

Preferred experience with spatial data Understanding of automated testing practices

Deep understanding of algorithmic optimization

Strong analytical and technical problem solving skills

Proficient coding and programming skills

Preferred experience supporting or developing novel algorithmic research