This Full time on site position offers great opportunities for career growth. TomTom is a integral leader in navigation, mapping, and traffic information. Join our dynamic team and vibrant culture to contribute to shaping the future of location technology. We are looking for an Applied Scientist to shape how AI understands and uses TomTom's location data. You'll develop machine learning models and design the systems that let LLMs reason accurately over maps, traffic, points of interest and routing. You'll turn open-ended product questions into well-defined scientific problems, validate solutions with rigorous evaluation, and work with AI and software engineers to bring them to production.
What you'll do:
LLM-data interfaces: Research and design how LLMs access and reason over TomTom's data. This covers retrieval strategies for structured and geospatial data, tool and API designs that LLMs can use reliably, text-to-query approaches, and grounding techniques.
Data representation: Develop representations that make location data usable by AI, such as embeddings for geospatial entities,



knowledge graphs and structured context formats.
Evaluation science: Define how we measure whether LLM-powered systems are right. Build benchmarks, factuality and hallucination metrics, and evaluation datasets for location-grounded tasks, and work with engineers to automate them.
Model research and development: Design, train and fine-tune ML and deep learning models, including LLMs, computer vision, time-series and graph-based methods, using large-scale, multi-modal data.
Problem framing: Work with product managers and stakeholders to identify high-impact opportunities and turn them into well-defined scientific problems with clear success metrics.
Research to production: Write production-quality code and work with AI and software engineers to take solutions from prototype to production, balancing accuracy, latency, cost and scalability.
State of the art: Keep up with the latest research in

📌 Applied Scientist at TomTom (Madrid)
🏢 TomTom
📍 Madrid