Senior Scientist - Machine Learning and Antibody Engineering

Barrington James; Cambridge; On-site; GBP 60,000 - 90,000; Full time

Job summary

Barrington James in Cambridge is seeking a Computational Antibody Engineer to apply machine learning, computational biology and protein engineering to the discovery, engineering and optimisation of therapeutic antibodies.

You will develop models on biological datasets, generate actionable insights and collaborate with experimental scientists to translate predictions into testable designs, contributing to library design, lead optimisation and candidate selection.

Qualifications

• PhD or equivalent 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.
• Strong understanding of protein or antibody sequence, structure, function and developability.
• Experience preparing, analysing 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.

Responsibilities

• Develop, train, validate and evaluate machine-learning models using internal and external biological datasets.
• Apply machine learning, statistical modelling and computational biology to antibody discovery, engineering, optimisation and candidate selection.
• Analyse 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 optimisation 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.

Skills

Machine learning

Computational biology

Bioinformatics

Protein engineering

Python programming

Data analysis

Communication skills

Education

PhD or equivalent in ML/Computational biology

Tools

Rosetta

Schrödinger

CCG suite

Python ML frameworks

Job description

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 optimisation of therapeutic antibodies.

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.

This is an excellent opportunity for a scientist with a strong background in machine learning, computational biology, bioinformatics or protein engineering who is passionate about applying computational approaches to real-world therapeutic discovery.

Key Responsibilities

• Develop, train, validate and evaluate machine-learning models using internal and external biological datasets.
• Apply machine learning, statistical modelling and computational biology to antibody discovery, engineering, optimisation and candidate selection.
• Analyse 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 optimisation 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.

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, analysing 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.

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 modelling, structure prediction, sequence design or molecular modelling.
• 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.