Applied Scientist.

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 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.

What you'll need:

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.