Barrington James
Computational Antibody Engineer
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Computational Antibody Engineer
An exciting opportunity is available for a Computational Antibody Engineer to apply machine learning, computational biology, and protein engineering approaches to the discovery, engineering, and optimization of therapeutic antibodies.
About the Role
Working as part of a multidisciplinary R&D team, you will develop and apply computational models to biological datasets, generate actionable insights, and work closely with experimental scientists to translate computational predictions into testable designs.
Key Responsibilities
- Develop, train, validate, and evaluate machine-learning models using internal and external biological datasets.
- Apply machine learning, statistical modeling, and computational biology to antibody discovery, engineering, optimization, and candidate selection.
- Analyze antibody sequence, structure, binding, functional, and developability data to support improvements in affinity, specificity, stability, solubility, and manufacturability.
- Work closely with experimental scientists to define scientific questions, develop validation strategies, and incorporate experimental results into iterative design cycles.
- Contribute to computationally guided library design, lead optimization, and project decision-making.
- Apply and evaluate emerging approaches in artificial intelligence, protein language models, generative protein design, and structure prediction.
- Communicate model outputs, uncertainty, limitations, and scientific recommendations clearly to multidisciplinary project teams.
- Maintain high standards of data quality, reproducibility, documentation, and scientific integrity.
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About You
- A PhD, or equivalent research experience, in machine learning, computational biology, bioinformatics, protein engineering, biophysics, or a related discipline.
- Postdoctoral or industry experience applying computational or machine-learning approaches to biological research.
- Demonstrable experience developing, validating, and interpreting machine-learning models.
- A strong understanding of protein or antibody sequence, structure, function, and developability.
- Experience preparing, analyzing, and quality-checking biological datasets.
- Strong Python programming skills and experience with relevant machine-learning and scientific-computing tools.
- Excellent analytical and problem-solving skills, with the ability to translate complex data into clear, evidence-based recommendations.
- Strong communication skills and experience working effectively within multidisciplinary scientific teams.
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Desirable Experience
Experience in one or more of the following would be advantageous:
- Therapeutic antibody discovery, engineering, or developability assessment.
- Antibody-antigen interactions, display technologies, high-throughput screening, or sequencing datasets.
- Protein language models, generative modeling, structure prediction, sequence design, or molecular modeling.
- Antibody or protein-design platforms such as Rosetta, Schrödinger, or the Chemical Computing Group suite.
- Integrating computational design into experimental design-build-test-learn cycles.
- Cloud computing, version control, and reproducible model-development workflows.
- Experience within pharmaceutical or biotechnology R&D environments.
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