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Why this role? The Predictive Biology & AI team at CITRE (Seville, Spain) is looking for a Scientist / Senior Scientist in Machine Learning to help push the boundaries of how AI can transform biomedical research.
This is a role for someone who enjoys both depth and impact: developing and training advanced models, exploring new scientific questions, and translating ideas into applications that matter.
Depending on your experience, you will either drive or take ownership of key research directions, while contributing to a collaborative and fast‐evolving environment.
CITRE is BMS's European center for computational innovation, where AI is embedded across the drug discovery pipeline.
The focus here is not just on building models, but on making them count—connecting cutting‐edge machine learning with real scientific and clinical decisions.
Some of the questions we are actively working on include:
How can foundation models be designed to capture the complexity of diseases such as cancer and neurodegeneration?
How do we combine multimodal data—omics, imaging (including MRI and other neuroimaging modalities), clinical signals, and text—into unified representations of biology?
How can predictive approaches anticipate patient response and support more precise, data‐driven therapies?
You will contribute directly to these efforts, helping turn emerging AI capabilities into practical tools for drug discovery and development.
What you will do Build and apply advanced machine learning models, with a focus on multimodal and foundation model approaches spanning transcriptomics, genomics, imaging (with particular emphasis on MRI and neuroimaging), and biomedical text
Train and evaluate modern deep learning architectures (e.g., Transformers, VAEs, ViTs, GNNs), including self‐supervised and generative methods
Develop models that support key drug discovery tasks such as target identification, biomarker discovery, and patient stratification
Work with large‐scale biological and imaging datasets, including structural and functional MRI
Create scalable and reproducible ML pipelines on cloud and/or HPC environments
Collaborate closely with computational and experimental scientists; contribute to or lead projects depending on your experience
Exchange ideas and best practices with teammates to collaboratively raise the quality of the work
Communicate insights clearly, ensuring your work informs both scientific understanding and strategic decisions
What you bring Ph.D. in Machine Learning, Bioinformatics, Computer Science, Statistics, Applied Mathematics, Physics, Electrical Engineering, or a related field
Strong hands‐on experience with modern machine learning and deep learning methods; exposure to foundation models or multimodal learning is a plus
Proficiency in Python and experience with PyTorch and the broader scientific Python ecosystem
Experience with cloud platforms (AWS, Azure, GCP) and/or HPC systems (e.g., SLURM)
Familiarity with Docker, Git, and reproducible research practices
Experience working with complex, large‐scale datasets; background in biological, clinical, or imaging data is advantageous
Evidence of impactful work (publications, projects, or applied contributions), appropriate to your career stage
Ability to work independently while thriving in collaborative, multidisciplinary settings
Clear and effective communication skills in English
Genuine interest in applying AI to biomedical and healthcare challenges
Desirable extras Familiarity with MRI and neuroimaging data (e.g., structural MRI, fMRI, diffusion imaging)
Experience with foundation model training strategies (masked, contrastive, autoregressive)
Experience with LLMs and related tools (e.g., HuggingFace, PyTorch Lightning, weights & biases)
Background in neuroscience and/or oncology
Previous experience in pharma, biotech, or healthcare research
Experience integrating multimodal datasets (omics, imaging, clinical, time-series)
Bristol‐Myers Squibb is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to gender identity, race, color, religion, sexual orientation, national origin or disability.
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