ZipRecruiter
Principal Scientist of AI-Driven Protein design
ZipRecruiter, Palo Alto, California, United States, 94306
Position Summary
Antibody AIDD (Principal Scientist) is responsible for leading a computational science team focused on biologics discovery. This role will provide critical support for the discovery and engineering of antibodies, fusion proteins, and other large-molecule therapeutics by applying and developing cutting-edge computational biology, structural biology, and AI technologies. The leader must be a technical expert capable of leading a team and collaborating closely with experimental teams in antibody engineering and protein sciences to solve core challenges in affinity, specificity, stability, and developability. Key Responsibilities
Team Leadership & Management:
Lead and develop a team of 2-4 computational biologists, fostering a scientifically rigorous and collaborative work environment. Project Scientific Leadership:
Serve as the core computational lead for biologics projects, directing the formulation and execution of computational strategies, including computational antigen design, antibody/protein design and optimization, and epitope prediction. Developability Assessment:
Lead the team in establishing and applying high-throughput computational predictive models to systematically assess and optimize the developability of candidates (e.g., immunogenicity, aggregation, viscosity) at early discovery stages to de-risk downstream development. Technology Innovation & Platform Development:
Track the latest advances in AI for biologics design; lead the development and validation of new algorithms and workflows for protein design, structure prediction, and property prediction, and promote their deployment on internal platforms. Cross-Functional Collaboration:
Collaborate closely with teams in Antibody Discovery, Protein Sciences, Bioanalytics, and Formulation Development to form an efficient "design-build-test-learn" R&D cycle. Basic Qualifications
Ph.D. in Computational Biology, machine learning, Structural Biology, Biophysics, or a related field. 5+ years of experience in biologics R&D within the pharmaceutical or biotechnology industry. Excellent programming skills in Python or R and experience with relevant bioinformatics and structural biology software. A solid theoretical and practical foundation in protein structure modeling and molecular dynamics simulations. Qualifications
Structural Modeling & De Novo Design:
Deep expertise in protein structure prediction (e.g., AlphaFold2/Multimer), protein-protein docking, and loop modeling. Biophysics Simulation:
Advanced knowledge and practical experience in running and analyzing all-atom molecular dynamics (MD) simulations of complex biologics (e.g., antibodies, bispecifics) to assess dynamics, stability, and aggregation propensity. AI for Biologics:
Familiarity and hands-on experience with modern AI methods, such as Protein Models (e.g., ESMFold, ProGen). Diffusion- or Generative Models:
Experience applying diffusion- or generative models for protein sequence and structure design. Graph Neural Networks (GNNs):
Knowledge of GNNs for protein function prediction or developability assessment. Bioinformatics & Multi-Omics Integration:
Proficiency in advanced sequence analysis, structural bioinformatics, and experience integrating multi-omics data (e.g., genomics, proteomics) for target identification and validation. Breadth & Depth of Project Experience:
A proven track record of leading computational efforts for antibody de novo design and affinity maturation. Verifiable contributions to solving specific developability issues for multiple biologic formats (e.g., mAbs, VHHs, bispecifics, ADCs).
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Antibody AIDD (Principal Scientist) is responsible for leading a computational science team focused on biologics discovery. This role will provide critical support for the discovery and engineering of antibodies, fusion proteins, and other large-molecule therapeutics by applying and developing cutting-edge computational biology, structural biology, and AI technologies. The leader must be a technical expert capable of leading a team and collaborating closely with experimental teams in antibody engineering and protein sciences to solve core challenges in affinity, specificity, stability, and developability. Key Responsibilities
Team Leadership & Management:
Lead and develop a team of 2-4 computational biologists, fostering a scientifically rigorous and collaborative work environment. Project Scientific Leadership:
Serve as the core computational lead for biologics projects, directing the formulation and execution of computational strategies, including computational antigen design, antibody/protein design and optimization, and epitope prediction. Developability Assessment:
Lead the team in establishing and applying high-throughput computational predictive models to systematically assess and optimize the developability of candidates (e.g., immunogenicity, aggregation, viscosity) at early discovery stages to de-risk downstream development. Technology Innovation & Platform Development:
Track the latest advances in AI for biologics design; lead the development and validation of new algorithms and workflows for protein design, structure prediction, and property prediction, and promote their deployment on internal platforms. Cross-Functional Collaboration:
Collaborate closely with teams in Antibody Discovery, Protein Sciences, Bioanalytics, and Formulation Development to form an efficient "design-build-test-learn" R&D cycle. Basic Qualifications
Ph.D. in Computational Biology, machine learning, Structural Biology, Biophysics, or a related field. 5+ years of experience in biologics R&D within the pharmaceutical or biotechnology industry. Excellent programming skills in Python or R and experience with relevant bioinformatics and structural biology software. A solid theoretical and practical foundation in protein structure modeling and molecular dynamics simulations. Qualifications
Structural Modeling & De Novo Design:
Deep expertise in protein structure prediction (e.g., AlphaFold2/Multimer), protein-protein docking, and loop modeling. Biophysics Simulation:
Advanced knowledge and practical experience in running and analyzing all-atom molecular dynamics (MD) simulations of complex biologics (e.g., antibodies, bispecifics) to assess dynamics, stability, and aggregation propensity. AI for Biologics:
Familiarity and hands-on experience with modern AI methods, such as Protein Models (e.g., ESMFold, ProGen). Diffusion- or Generative Models:
Experience applying diffusion- or generative models for protein sequence and structure design. Graph Neural Networks (GNNs):
Knowledge of GNNs for protein function prediction or developability assessment. Bioinformatics & Multi-Omics Integration:
Proficiency in advanced sequence analysis, structural bioinformatics, and experience integrating multi-omics data (e.g., genomics, proteomics) for target identification and validation. Breadth & Depth of Project Experience:
A proven track record of leading computational efforts for antibody de novo design and affinity maturation. Verifiable contributions to solving specific developability issues for multiple biologic formats (e.g., mAbs, VHHs, bispecifics, ADCs).
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