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Principal Scientist, Computational Designer (Biologics & Peptides)

• Initiate, drive and support drug discovery projects as an active member and leader of highly interdisciplinary, cross‑organizational teams

• Apply structural and biological knowledge to connect targets with suitable modalities. Define tailored lead-finding strategies by characterizing protein-protein and protein-ligand interactions, identifying druggable pockets and epitopes, and designing biomolecules.

• Design and optimize antibodies and peptides using structure-based and sequence-based computational and AI approaches, in partnership with antibody engineering teams and machine learning solution providers.
• Build and apply modeling pipelines for affinity, specificity, stability, developability, and manufacturability-related properties (e.g., aggregation/solubility risk, PTM liabilities).
• Generate and triage design hypotheses by integrating structural data (X-ray/cryo-EM), sequence/omics data, biophysical/assay results, and literature.
• Develop automated workflows for candidate generation, scoring, and selection and data integration across programs.
• Collaborate with wet-lab teams to define design goals, interpret experimental data, iterate designs and identify opportunities for acceleration with AI capabilities.
• Partner with IT/HPC stakeholders to ensure appropriate compute infrastructure, software environments, and access patterns to deliver reliable modeling workflows and analytics.
• Be a driver of innovation and take ownership with your peers to further develop and innovate antibody, peptide and conjugate lead discovery and optimization.
• Communicate results to cross-functional stakeholders through concise reports, visualizations, and presentations.

• PhD in Computational Biology, Biophysics, Bioinformatics, Chemical/Biological Engineering, Structural Biology, or related discipline.
• Proven scientific and project leadership capabilities in cross‑functional research environments
• Expertise in protein and peptide structure modeling, antibody design, molecular dynamics, free-energy and physics-based scoring, and machine-learning-guided design, including proficiency with state-of-the-art AI tools for co-folding, structure prediction, and de novo binder generation
• Ability to work with and interpret large experimental data sets (binding/functional assays, biophysical characterization, developability screens).
• Excellent problem-solving skills and the ability to work independently and collaboratively in a cross-functional environment.
• Excellent communication and negotiation skills, pronounced intercultural competence and strong team orientation
• Enjoy working in self‑organized teams, taking ownership and accountability for the full value chain

• 5+ years professional experience (industry or equivalent) in peptide and therapeutic antibody drug discovery with deep knowledge of the scientific literature and industry practices
• Experience working with large-scale compute environments (HPC, cloud), containers, and workflow orchestration
• Understanding of developability considerations for biologics (immunogenicity risk, viscosity, stability, liabilities) and relevant in silico assessments
• Familiarity with antibody or peptide conjugate modalities (e.g., antibody–drug conjugates, oligonucleotide, radionuclide or peptide payloads)