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AD/Director AI Protein Design

BioTalent Ltd
Redmond, WA Full Time
POSTED ON 2/8/2025
AVAILABLE BEFORE 5/3/2025

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

  • PhDin 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.
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