Applied Scientist

. Research and design how LLMs access and reason over TomTom's structured and geospatial data .

Posted 9/25/2026full-timeMadrid • SpainMid-LevelSeniorWebsite

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformNumpyPandasPythonPyTorchScikit-LearnSpark

Key responsibilities & impact

Research and design how LLMs access and reason over TomTom's structured and geospatial data
Develop retrieval strategies, tool and API designs, text-to-query approaches, and grounding techniques
Develop AI-ready representations of location data, including geospatial embeddings, knowledge graphs and structured context formats
Define benchmarks, factuality and hallucination metrics, and evaluation datasets for location-grounded tasks
Design, train and fine-tune ML and deep learning models, including LLMs, computer vision, time-series and graph-based methods
Work with product managers and stakeholders to frame high-impact opportunities as scientific problems with clear success metrics
Write production-quality code and collaborate with AI and software engineers to move solutions from prototype to production
Balance accuracy, latency, cost and scalability
Keep up with research in LLMs, retrieval and agentic systems and adapt techniques to TomTom's problems
Communicate findings to technical and non-technical audiences and contribute to scientific culture

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
Experience 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

Comp & perks