Description du poste
**About us**
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Owkin is an agentic AI company pioneering Biological Artificial Superintelligence to solve problems in biology where human researchers alone have failed.
Owkin builds K Pro - an AI scientist for pharmaceutical research and strategic decision-making. K Pro orchestrates a suite of AI skills and tools to decode complex biology, accelerate research, and dramatically increase productivity.
K Pro is built on Owkin’s unrivalled multimodal patient data network, state-of-the-art AI for biology and a decade of experience working with pharmaceutical partners.
***Position is based in our Paris office or remotely in France, UK, Germany.***
***Please submit your CV in English***
**About the role:**
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We are seeking a driven **Research Scientist** with hands-on expertise in Foundation Models (FMs) and Multimodal AI to help drive the next phase of our machine learning platform.
In this role, you will bridge cutting-edge AI research and practical application. Rather than training massive base models from scratch, your focus will be on fine-tuning, adapting, and aligning open-weights foundation models across diverse data modalities (e.g. structured tabular data, single-cell scRNA-seq, image representations, and natural language) to build multimodal systems.
**In particular, you will be responsible for:**
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* **Model adaptation and optimization:** Adapt, fine-tune, and evaluate state-of-the-art open-source foundation models (e.g., domain-specific transformers, vision-language models) using fine-tuning and transfer learning strategies.
* **Multimodal integration:** Design and implement cross-modal alignment architectures (e.g., contrastive learning, projection heads, unified embedding spaces) to fuse structured, sequential, and text/image data layers.
* **Pipeline and evaluation engineering:** Build rigorous, domain-tailored benchmarking frameworks and evaluation pipelines to validate model reliability, generalization, and downstream task performance.
* **Applied R&D collaboration:** Work closely with cross-functional teams (data engineers, domain specialists, and software engineers) to integrate adapted models into scalable production and inference workflows.
* **Low-hanging fruit innovation:** Identify high-impact, incremental improvements in data pre-processing, embedding representation, and fine-tuning to maximize model performance without unnecessary computational overhead.
**About you**
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Required qualifications / experience:
* **Education:** Ph.D. or Master’s degree in Computer Science, Machine Learning, Mathematics or a related quantitative field.
* **Experience:** 2-4 years of post-degree experience applying deep learning and foundation models to complex datasets.
* **Foundation models:** Demonstrated experience adapting and fine-tuning foundation models.
* **Multimodal learning:** Proven experience with cross-modal representation learning, j