Applied Scientist: AI for Maps & Location Tech (Hybrid)

TomTom; Madrid; Híbrido; EUR 60.000 - 100.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Personal development budget

Learning days paid leave

Access to O'Reilly courses

Flexible work arrangement

Descripción de la vacante

TomTom is hiring an Applied Scientist to shape how AI understands and uses location data. You will develop ML models and systems enabling LLMs to reason over maps, traffic, POIs and routing, turning product questions into well-defined scientific problems.

You will work with AI and software engineers to take solutions from prototype to production, balancing accuracy, latency, and cost, and contribute to a strong scientific culture.

Formación

• Strong foundations in ML, DL, statistics and optimization.
• Experience with PyTorch or JAX and Python stack.
• Experience with LLMs, CV, time-series, graphs, or RL.
• Ability to design experiments and draw conclusions from data.
• Experience with large-scale datasets and distributed computing.

Responsabilidades

• Research and design how LLMs access TomTom data and grounding techniques.
• Develop representations for geospatial data and context formats.
• Define evaluation metrics and benchmarks for location-grounded tasks.
• Design, train and fine-tune ML/ DL models including LLMs and multi-modal methods.
• Turn product questions into scientific problems with clear success metrics.
• Write production-quality code and work with engineers to deploy solutions.
• Stay updated with SOTA in LLMs, retrieval and agentic systems.
• Communicate findings to technical and non-technical audiences.

Conocimientos

ML fundamentals

Deep learning

Statistics

Optimization

PyTorch

JAX

Communication skills

Publications

Distributed computing

Educación

Bachelor's / Master / PhD in CS or related

Herramientas

Spark/Databricks

Cloud ML platforms

Python stack (NumPy pandas scikit-learn)

Descripción del empleo

TomTom is hiring an Applied Scientist to shape how AI understands and uses location data. You will develop ML models and systems enabling LLMs to reason over maps, traffic, POIs and routing, turning product questions into well-defined scientific problems.

You will work with AI and software engineers to take solutions from prototype to production, balancing accuracy, latency, and cost, and contribute to a strong scientific culture.