> Markdown version of [/jobs/ext/319091-research-scientist-information-retrieval-recommendation-systems](https://www.wearedevelopers.com/jobs/ext/319091-research-scientist-information-retrieval-recommendation-systems). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Research Scientist (Information Retrieval & Recommendation Systems)

- **Company:** NielsenIQ Ver todas las vacantes
- **Location:** Madrid, Spain
- **Salary:** €46,100.0 - €51,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Big Data, Elasticsearch, Graph Database, Information Retrieval, Python (Programming Language), Machine Learning, Natural Language Processing, Recommender Systems, SQL Databases, Pytorch, Large Language Models, Deep Learning, Generative AI, Git, Pandas, Scikit Learn, Information Technology, HuggingFace, Production Code, Machine Learning Operations, GPT, Databricks
- **Published:** June 22, 2026
- **Apply:** https://opcionempleo.com/jobad/es62611524ce75cd4db9af027f0688d73a

## About the Role

Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Mathematics, Physics, or a related quantitative discipline.
3-6 years of experience in Machine Learning, Deep Learning, or AI research.
Experience with Python, PyTorch, Hugging Face, Pandas, Scikit-learn, and Git.
Experience training Transformer models, embeddings, and modern NLP techniques.
Experience training custom SLMs and /or small reasoning models.
Familiarity with Information Retrieval concepts and vector databases (e.g., FAISS, ChromaDB, Pinecone).
Understanding of contrastive learning and representation learning techniques.
Experience performing EDA with large datasets and writing production-quality code.
Ability to understand scientific papers and implement research ideas into practical solutions.
Strong analytical, problem-solving, and communication skills.
Ability to work effectively within a diverse, international team.
Good to Have:
Publications in AI, NLP, Information Retrieval, or Recommendation Systems.
Experience with RAG architectures, LLMs, and agent-based frameworks.
Experience with LangChain or LangGraph.
Experience with Knowledge Graphs.
Experience with MLOps practices.
Experience with Databricks, SQL, Elasticsearch.
Experience in retail, consumer intelligence, ecommerce, or FMCG domains.

## Description

* Research, evaluate, and adapt state-of-the-art techniques to solve large-scale AI problems.

Fine-tune and evaluate Transformer-based models and embedding models for domain-specific applications.
Contribute to research initiatives in Information Retrieval, Product Matching, Recommendation Systems, and Generative AI.
Collaborate with technical colleagues on the integration of ML solutions.
Contribute to scientific publications, patents, and innovation initiatives. Maintain software and data assets following high-quality engineering standards
Develop and apply machine learning innovations to business problems with moderate technical supervision.
Assess experimental results and determine their applicability to real-world business challenges.
Understand stakeholder requirements and communicate results and recommendations clearly and effectively.
Perform feasibility studies and analyze data to identify appropriate technical solutions.
Develop scalable, reproducible, and maintainable machine learning solutions.


## Related Videos

- [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail)
- [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94)
- [Advanced Typing in TypeScript](https://www.wearedevelopers.com/videos/496-advanced-typing-in-typescript)
- [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension)
- [Getting to Know Your Legacy (System) with AI-Driven Software Archeology](https://www.wearedevelopers.com/videos/1437-getting-to-know-your-legacy-system-with-ai-driven-software-archeology)
- [How We Built a Machine Learning-Based Recommendation System (And Survived to Tell the Tale)](https://www.wearedevelopers.com/videos/752-how-we-built-a-machine-learning-based-recommendation-system-and-survived-to-tell-the-tale)

## Related Articles

- [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)
- [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)
- [7 Most Popular Web Developer Jobs in Europe](https://www.wearedevelopers.com/magazine/163-7-most-popular-web-developer-jobs-in-europe)
- [The Most Popular IT Jobs on the Market](https://www.wearedevelopers.com/magazine/376-the-most-popular-it-jobs-on-the-market)
- [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts)
- [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)