Senior Research Scientist (Applied Research - Agentic AI, SLMs & GenAI Research)
External
Nielseniq · Madrid, SpainPrepare for this interview
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Requirements
- Understanding of scalable AI/ML system design and implementation realities
- Familiarity with deployment-oriented thinking for GenAI solutions
- Experience building or evaluating multi-component AI systems beyond single-model use cases
- Experience with reusable research patterns that can transition into practical application
- Exposure to model efficiency, optimization, or performance trade-offs
- Required: Python, PyTorch, Transformers
- Preferred: Hugging Face ecosystem, agentic AI / orchestration frameworks, evaluation frameworks, fine-tuning pipelines, scalable ML platforms
- Not Required: Deep software engineering specialization or advanced stakeholder management experience
- Role Emphasis:
- Heavy focus on applied research in agentic AI capabilities and SLM-based solutions
- Drives experimentation, modeling strategy, and validation methods in this area
- Helps mature agentic approaches beyond early PoCs into more robust and repeatable patterns
- Contributes to the research direction of the applied ML/AI research team through hands-on exploration and technical leadership
- Works with others in the team to ensure promising research directions remain realistic and transferable
- #LI-Hybrid
- Pay Range: 55,300.00 - 65,000 EUR Annual - This posting is for a current vacancy
Benefits
Additional Information
Focus: Researching agentic AI capabilities and Small Language Models (SLMs), with emphasis on experimentation, modeling strategy, validation methods, and maturing approaches beyond proof of concept toward robust, reusable patterns. Core Requirements: Strong background in applied machine learning / AI research Deep hands-on experience with GenAI systems and architectures Experience with Small Language Model (SLM) fine-tuning and adaptation Strong understanding of agentic AI concepts, including multi-step reasoning, tool use, orchestration, or workflow-based model behavior Strong grounding in classical machine learning as well as modern deep learning approaches Ability to design rigorous experiments and evaluate novel AI system behavior Strong communication skills and ability to engage with technical and non-technical stakeholders in English
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