Computational Designer Biologics & Peptides

At Bayer we’re visionaries, driven to solve the world’s toughest challenges and striving for a world where ,Health for all, Hunger for none’ is no longer a dream, but a real possibility. We’re doing it with energy, curiosity and sheer dedication, always learning from unique perspectives of those around us, expanding our thinking, growing our capabilities and redefining ‘impossible’. There are so many reasons to join us.

If you’re hungry to build a varied and meaningful career in a community of brilliant and diverse minds to make a real difference, there’s only one choice.

In this role, you initiate, drive, and support drug discovery projects at the intersection of structural biology, biophysics, and AI-enabled design. You connect targets with the right modalities and define tailored lead-finding strategies for antibodies, peptides, and conjugates. If you thrive in highly interdisciplinary environments and want to take end-to-end ownership from hypothesis to candidate selection, this position offers a high-impact platform to shape next-generation biologics discovery.

• 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 and define tailored lead-finding strategies (e.g., characterize protein–protein and protein–ligand interactions, identify druggable pockets and epitopes, design 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, develop ability, 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 scientific literature
• Develop automated workflows for candidate generation, scoring, selection, and data integration across programs
• Collaborate with wet-lab teams to define design goals, interpret experimental data, iterate designs, and identify acceleration opportunities with AI capabilities
• Partner with IT/HPC stakeholders to ensure fit-for-purpose compute infrastructure, software environments, and access patterns to deliver reliable modeling workflows and analytics; communicate outcomes via concise reports, visualizations, and presentations

• PhD in Computational Biology, Biophysics, Bioinformatics, Chemical/Biological Engineering, Structural Biology, or a related discipline
• Several years of professional experience (industry or equivalent) in peptide and therapeutic antibody drug discovery, with strong familiarity with the scientific literature and industry practices
• 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, develop ability screens)
• Experience with large-scale compute environments (HPC/cloud), containers, and workflow orchestration is a plus
• Understanding of develop ability considerations for biologics (immunogenicity risk, viscosity, stability, liabilities) and relevant in…