Applied Scientist

• Bachelor’s, Master's or PhD in Computer Science, Machine Learning, Statistics, Mathematics, Physics or a related quantitative field, or equivalent professional experience. 
• Strong foundations in machine learning, deep learning, statistics and optimization. 
• Hands-on experience with modern deep learning frameworks such as PyTorch or JAX, and with the Python scientific stack (NumPy, pandas, scikit-learn). 
• Experience with one or more of: LLMs and generative AI, computer vision, time-series forecasting, graph neural networks, or reinforcement learning. 
• Proven ability to design experiments, define meaningful metrics and draw sound conclusions from data. 
• Experience working with large-scale datasets and distributed computing (e.g., Spark, Databricks, or cloud ML platforms on Azure, AWS or GCP). 
• Ability to write clean, maintainable code and to work with engineers toward production deployment. 
• Excellent communication skills, with the ability to explain complex technical ideas to diverse audiences. 

• A track record of publications at top-tier venues (e.g., NeurIPS, ICML, CVPR, KDD) is a plus. 
• Experience with geospatial, mapping, mobility or sensor data is a plus, but not required.

A competitive compensation package, of course.

Time and resources to grow and develop, including a personal development budget and paid leave for learning days, as well as paid access to e-learning resources such as O'Reilly and LinkedIn Learning.

Time to support life outside of work, with enhanced parental leave plus paid leave to care for loved ones and volunteer in local communities.

Work flexibility, where TomTom'ers, in agreement with their manager and team, use both the office and home to focus, collaborate, learn and socialize. It's all about getting the best out of both worlds - we ask TomTom'ers to come to the office two days a week, and the remaining three are free to be worked in either location.

Improve your home office with a setup budget and get extra support with a monthly allowance.

Enjoy options to work from your home country and abroad for a set number of days each year, to visit family and friends, or to simply explore the world we're mapping.

Take the holidays you want with a competitive holiday plan, plus an extra day off to celebrate your birthday.

Join annual events like our Hackathon and DevDays to bring your ideas to life with talented teammates from around the world.

Become a part of our inclusive global culture and have the chance to collaborate with a diverse community - we have over 80 nationalities at TomTom!

Find out more about our global benefits and enjoy additional local benefits tailored to your location.

TomTom is a global 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.

• 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 LLMs, retrieval and agentic systems, and adapt promising techniques to TomTom's problems. 
• Knowledge sharing: Communicate findings clearly to technical and non-technical audiences, and help build a strong scientific culture. External publications and patents are encouraged.