A pioneering biotech company is seeking a Director of AI-Driven Protein Design to lead computational strategies for the discovery and engineering of novel biologics. This role will focus on integrating AI-driven design principles, including generative models and diffusion-based approaches, to optimize protein and antibody therapeutics. The successful candidate will be instrumental in shaping next-generation computational pipelines, leveraging cutting-edge machine learning and structural modeling techniques to accelerate drug discovery.

Key Responsibilities

• Develop and enhance AI-powered protein design pipelines, utilizing structure-based and sequence-based modeling to optimize biologic therapeutics.
• Implement diffusion models and generative AI techniques to design novel peptides, antibodies, and de novo proteins.
• Apply computational methodologies to predict, refine, and optimize molecular interactions, stability, and function.
• Work closely with experimental teams to validate in silico predictions, integrating insights from high-throughput screening and yeast display platforms.
• Contribute to lead optimization by leveraging multi-parametric molecular property modeling and data-driven approaches.
• Analyze, interpret, and present findings to internal and external stakeholders, guiding strategic decision-making in therapeutic development.
• Stay ahead of emerging technologies in computational protein design, generative modeling, and AI-driven molecular engineering.

Qualifications

• PhD in bioengineering, biophysics, computational biology, AI/ML, or a related field.
• Extensive experience with generative AI, diffusion models, and large-scale molecular simulations applied to protein design.
• Hands-on expertise in state-of-the-art macromolecular modeling tools such as AlphaFold, RoseTTAFold, RFdiffusion, ProteinMPNN, GROMACS, MOE, Schrödinger, or similar platforms.
• Strong background in computational antibody design, including screening and ranking techniques (ELISA, flow cytometry, BLI/OCTET, etc.).
• Track record of integrating deep learning-based approaches with experimental validation in a biotech or pharma setting.
• Experience in leading cross-functional teams and applying AI/ML-driven solutions to biologics discovery is highly desirable.