ZipRecruiter
AI Antibody Design Research Scientist
ZipRecruiter, Redwood City, California, United States, 94061
Job Description
Position Overview
Our client is a Stealth Biotechnology company whose core mission is to discover and develop differentiated biologics to treat immunological and other diseases. Our client is seeking a highly motivated AI Antibody Design Research Scientist to join the team. The successful candidate will leverage cutting-edge artificial intelligence and machine learning approaches to advance antibody discovery and design. This role offers the opportunity to work at the intersection of AI, structural biology, and therapeutic development. Key Responsibilities:
Research & Development
Develop and implement novel AI/ML algorithms for antibody design, optimization, and engineering Apply and improve protein structure prediction models for antibody-antigen interactions Design computational workflows for high-throughput antibody screening and optimization Integrate multi-modal data including sequence, structure, and experimental data Iteratively fine tune/retrain models based on experimental validation and feedback from wet lab teams Collaboration & Innovation
Stay current with rapidly evolving AI antibody design literature and methodologies Evaluate and implement state-of-the-art models and tools in the field Collaborate closely with experimental teams to validate computational predictions Effectively communicate complex computational concepts and results to experimental scientists Integrate experimental data as feedback to iteratively improve and fine tune/retrain AI models Present research findings at scientific conferences and publish in peer-reviewed journals Required Qualifications and Experience
Ph.D. in Computational Biology, Bioinformatics, Computer Science, Physics, Chemistry, or related field 1+ years of postdoctoral or industry experience in AI antibody design Demonstrated experience in either: protein structure modeling and prediction algorithms, or antibody design, engineering, or computational immunology Experience with diffusion models, pairformer architecture and antibody models for structure prediction and antibody design
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Our client is a Stealth Biotechnology company whose core mission is to discover and develop differentiated biologics to treat immunological and other diseases. Our client is seeking a highly motivated AI Antibody Design Research Scientist to join the team. The successful candidate will leverage cutting-edge artificial intelligence and machine learning approaches to advance antibody discovery and design. This role offers the opportunity to work at the intersection of AI, structural biology, and therapeutic development. Key Responsibilities:
Research & Development
Develop and implement novel AI/ML algorithms for antibody design, optimization, and engineering Apply and improve protein structure prediction models for antibody-antigen interactions Design computational workflows for high-throughput antibody screening and optimization Integrate multi-modal data including sequence, structure, and experimental data Iteratively fine tune/retrain models based on experimental validation and feedback from wet lab teams Collaboration & Innovation
Stay current with rapidly evolving AI antibody design literature and methodologies Evaluate and implement state-of-the-art models and tools in the field Collaborate closely with experimental teams to validate computational predictions Effectively communicate complex computational concepts and results to experimental scientists Integrate experimental data as feedback to iteratively improve and fine tune/retrain AI models Present research findings at scientific conferences and publish in peer-reviewed journals Required Qualifications and Experience
Ph.D. in Computational Biology, Bioinformatics, Computer Science, Physics, Chemistry, or related field 1+ years of postdoctoral or industry experience in AI antibody design Demonstrated experience in either: protein structure modeling and prediction algorithms, or antibody design, engineering, or computational immunology Experience with diffusion models, pairformer architecture and antibody models for structure prediction and antibody design
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