Requirements
- PhD in Computational Biology, Machine Learning, Bioinformatics, Genomics, or a related field
- Deep understanding of the biopharma R&D value chain (target discovery to clinical trials)
- Deep experience in biological data modalities, notably multi-omics (single-cell, transcriptomics, proteomics) and histopathology slides
- Extensive knowledge in modern ML architectures and paradigms like Transformers (ViT), GNNs, foundation models with embedding-based prediction heads (e.g., ABMIL), interpretability methods, and model evaluation & validation principles
- Proven experience in designing end-to-end ML solutions for complex biological problems, including problem framing, data strategy and model design & evaluation
- Strong fluency in Python and deep learning frameworks (PyTorch, JAX)
- Proven experience navigating complex, matrixed organizations (Big Pharma experience is a plus)
- Ability to distill complex client complaints or requests into clear, prioritized product requirements
Nice to Have
- Experience in a startup or innovative environment, showing adaptability and proactiveness
- A strong existing network within Global Top 20 Pharma R&D
- Experience specifically with "Foundation Models" or Generative AI in a biological context
- Track record of high-impact publications (Nature, Cell, NeurIPS)
Benefits
- A collaborative and mission-driven work environment
- Competitive salary and equity package
- Flexible work arrangements, including remote options
- Opportunities for professional growth and leadership development
- Shape the future of biology and AI by contributing to groundbreaking work
Work Arrangement
Hybrid
Additional Information
- Submit your CV in English
- Transparent and collaborative interview process
- Offer is contingent upon the successful completion of a reference check
