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TomTom
3.8
Applied Scientist Intern
Madrid
Easy Apply
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11d
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TomTom
3.8
Applied Scientist
Madrid
Easy Apply
Working familiarity with several of the following (in depth experience is not needed): algorithm design for geospatial and geometric problems (polygon geometry,……
28d
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Barcelona Supercomputing Center
3.7
Intern in Computational Social Sciences (R0)
Barcelona
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GMV
4.3
Internship – AI Applications for Space
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Collaborate in the development and validation of Artificial Intelligence models for the identification and classification of radio frequency signals in space……
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Corteva Agriscience
4.0
Engineering & Maintenance Intern
España
This is a low‑risk, high‑impact opportunity to gain hands‑on experience at the intersection of engineering, maintenance, digitalization and data analytics,……
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Satlantis
4.0
AI ENGINEER
España
You have an English level of at least B2, allowing you to contribute to international projects including ESA and NASA.…
30d+
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TomTom

3.8

Applied Scientist Intern

Madrid

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The Applied Scientist Intern in the MAPS POIs team contributes to the research, experimentation, and development of data-driven and machine-learning solutions that enhance the accuracy, coverage, and usability of TomTom's maps and Points of Interest products. This internship gives you hands-on experience applying scientific and analytical methods to real-world problems at scale, working alongside Applied Scientists and Engineers on challenges that directly impact TomTom's products.

What you'll do:

You will work embedded in the POI team on concrete research and engineering tasks, with guidance from senior engineers/scientists. You will:

Explore and experiment with ML/AI approaches to solve POI-domain problems such as entity matching, address parsing, data quality assessment, or coverage analysis
Implement and evaluate models and algorithmic solutions on real-world, large-scale geospatial datasets
Design and run experiments, analyze results, and translate findings into clear insights, recommendations and implementation
Be part of the development of data pipelines and tooling that support model training, evaluation, and analysis
Collaborate with Applied Scientists, Engineers, and Product stakeholders to understand requirements and integrate your work into the broader team workflow
Document experiments, methodologies, and results clearly to support knowledge sharing within the team

What you'll need:

Currently enrolled in a Master's programme in Computer Science, Data Science, Artificial Intelligence, Machine Learning, or a related field
Solid grounding in machine learning fundamentals — supervised/unsupervised learning, model evaluation, feature engineering
Hands-on experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn (from coursework, research, or personal projects)
Programming proficiency in Python; experience with data manipulation libraries (pandas, NumPy, Spark is a plus)
Familiarity with NLP or embedding-based methods (e.g., Sentence Transformers, BERT-based models) is a strong plus
Interest in geospatial data, POI systems, addressing, or location intelligence
Analytical mindset with the ability to design experiments, interpret results critically, and communicate findings clearly
Collaborative and curious — comfortable asking questions, working iteratively, and learning from feedback

What you'll learn:

By the end of the internship you will have:

Worked on production-scale geospatial and POI data with real business impact
Gained experience in the full ML experimentation cycle - from problem framing and data analysis to model development and evaluation
Deepened your understanding of applied ML in a domain where data quality, scale, and semantic complexity are central challenges
Collaborated in a cross-functional team of scientists, engineers, and product managers

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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Working here doesn’t have to be a secret

3.8

Recommend to a friend
Approve of CEO
CEO: Harold Goddijn
825 Ratings

TomTom

3.8

Applied Scientist Intern

Madrid

TomTom

3.8

Applied Scientist Intern

Madrid

Is your resume a good match?

Use AI to find out how well the skills on your resume fit this job description.

What you'll do:

You will work embedded in the POI team on concrete research and engineering tasks, with guidance from senior engineers/scientists. You will:

Explore and experiment with ML/AI approaches to solve POI-domain problems such as entity matching, address parsing, data quality assessment, or coverage analysis
Implement and evaluate models and algorithmic solutions on real-world, large-scale geospatial datasets
Design and run experiments, analyze results, and translate findings into clear insights, recommendations and implementation
Be part of the development of data pipelines and tooling that support model training, evaluation, and analysis
Collaborate with Applied Scientists, Engineers, and Product stakeholders to understand requirements and integrate your work into the broader team workflow
Document experiments, methodologies, and results clearly to support knowledge sharing within the team

What you'll need:

Currently enrolled in a Master's programme in Computer Science, Data Science, Artificial Intelligence, Machine Learning, or a related field
Solid grounding in machine learning fundamentals — supervised/unsupervised learning, model evaluation, feature engineering
Hands-on experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn (from coursework, research, or personal projects)
Programming proficiency in Python; experience with data manipulation libraries (pandas, NumPy, Spark is a plus)
Familiarity with NLP or embedding-based methods (e.g., Sentence Transformers, BERT-based models) is a strong plus
Interest in geospatial data, POI systems, addressing, or location intelligence
Analytical mindset with the ability to design experiments, interpret results critically, and communicate findings clearly
Collaborative and curious — comfortable asking questions, working iteratively, and learning from feedback

What you'll learn:

By the end of the internship you will have:

Worked on production-scale geospatial and POI data with real business impact
Gained experience in the full ML experimentation cycle - from problem framing and data analysis to model development and evaluation
Deepened your understanding of applied ML in a domain where data quality, scale, and semantic complexity are central challenges
Collaborated in a cross-functional team of scientists, engineers, and product managers

See company reviews

Working here doesn’t have to be a secret

3.8

Recommend to a friend
Approve of CEO
CEO: Harold Goddijn
825 Ratings

Are you open to new opportunities?

6 Applied machine learning intern jobs in Spain

What you'll do:

What you'll need:

What you'll learn:

Working here doesn’t have to be a secret

What you'll do:

What you'll need:

What you'll learn:

Working here doesn’t have to be a secret