At TomTom, the Maps team sits at the heart of our vision to build the world's most accurate, fresh, and intelligent map. As an Applied Scientist, you will help evolve the Orbis Road Model Map and the Orbis Lane Model Map by transforming high‑fidelity, high‑volume data into map content that powers next‑generation navigation, automated driving, and high‑quality Guidance for millions of drivers.

You will t play a pivotal role in building navigation-related map content from a collection of massive geospatial data sources using ML algorithms and solutions. — fueling products and partnerships that rely on TomTom's map as a competitive differentiator, from global OEMs to cutting‑edge automated driving programs.

• Design and develop algorithms that extract actionable insights from massive, imperfect datasets, turning raw geospatial data into intelligence that powers the future of map-making technology.

• Work with one of the largest traffic data streams worldwide, applying your expertise to decode complex datasets and improve mobility systems.

• Take ownership of the full lifecycle of software projects, ensuring smooth transitions from research to production-level implementation.

• Support and improve production environments to guarantee operational excellence of the systems you build.

• Work closely with Software Engineers and Product Managers on the team.

• A university-level degree in Machine Learning, Computer Science, or a relevant field.

• 5+ years of industry experience developing production-grade code, with proficiency in Java and Python.

• Proven track record in solving business problems through ML solutions (e.g. Computer Vision, classic ML algorithms, etc.)

• Strong skills in Software Engineering, both in greenfield projects and optimizing existing systems.

• You’re a collaborative team player who thrives in an international environment, and you have strong written and verbal communication skills in English.

• Experience in handling large volumes of Geospatial data

• Familiarity with DevOps practices and cloud platforms like Azure.

• Familiarity with Big Data pipelines (e.g. Spark/Kafka)