Research Scientist – Foundation Models and Multimodal AI

About us 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:  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: 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 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, joint embedding alignment (e.g., CLIP-style architectures), or vision-language models. Technical stack: Advanced proficiency in Python and modern deep learning frameworks ( PyTorch , Hugging Face ecosystem). Hands-on experience with fine-tuning techniques. Familiarity with data engineering tools for handling large-scale or multi-modal datasets (Parquet, AnnData, HDF5).  Preferred qualifications/bonus: Experience working with biological or omics data (e.g., scRNA-seq, spatial transcriptomics, clinical and/or histology data). Experience deploying or scaling models using containerized environments (Docker, Kubernetes) and cloud infrastructure (AWS/GCP). Track record of open-source contributions or peer-reviewed publications at top ML conferences (NeurIPS, ICML, ICLR, CVPR) or domain-specific journals.    #LI-HB1 What we offer Flexible work organization Friendly and informal working environment Opportunity to work with an international team with high technical and scientific backgrounds Recruitment Process & Security Please complete the form and submit your CV. Owkin is an Equal Opportunity Employer. All qualified