Computational Structural Biologist

Compensation: Competitive, based on experience

• Bindbridge is advancing sustainable agriculture through AI-powered molecular glue discovery and design.
• Backed by Speed invest and Nucleus Capital, we are building a computational platform to bring targeted protein degradation to agriculture.
• Our first goal is to discover herbicides that revolutionise crop protection while minimising environmental impact.

• We are looking for a Computational Structural Biologist to help develop computational approaches for understanding and designing molecular glue-induced protein interactions.
• You will combine structural modelling and prediction with experimental data to develop and test hypotheses about protein complexes and binding.
• This is a hands-on coding role. You will develop and maintain scientific software, data pipelines and structured datasets that connect predicted structures, experimental results and computational analyses.
• Working across our science, AI and software teams, you will turn research questions into clearly defined computational studies, evaluate the results and communicate their implications.
• Projects may draw on data from XL-MS, HDX-MS, SAXS, cryo-EM, X-ray crystallography and biophysical assays. You are not expected to know every method, but you should be willing to learn how unfamiliar data can constrain, validate or challenge structural models, with support from our scientists, collaborators and advisers.

• Lead computational studies of protein structures, complexes and molecular glue-induced interactions using methods such as structure prediction, co-folding, docking, molecular dynamics and integrative modelling.
• Work with experimental scientists and advisers to incorporate structural and biophysical evidence into models, understand its limitations and identify useful follow-up experiments.
• Write, test and maintain reusable scientific software for structural modelling, analysis and evaluation, contributing directly to shared codebases and working with AI and software engineers to integrate, deploy and scale it.
• Design and build data pipelines that ingest, validate, harmonise and version structural, sequence, experimental and computational data.
• Develop and maintain data models and databases that connect proteins, structures, experiments, predictions and results, with clear quality controls, metadata and provenance.
• Design benchmarks and evaluations that show where computational approaches work, where they fail and how confidently their results can be used.
• Document methods, assumptions, limitations and results, then communicate their scientific implications across disciplines.

• A strong foundation in computational structural biology, structural bioinformatics or a closely related field, including an understanding of protein structure, interactions and conformational behaviour, gained through postgraduate study or equivalent professional experience.
• Experience using computational methods to investigate protein structures or complexes and drawing conclusions from the results.
• Strong programming and software engineering skills with experience developing tested, maintainable scientific software.
• Experience building data pipelines and working with databases or structured scientific data systems, including validation, metadata, provenance and versioning.
• The ability to define a research question, select an appropriate computational approach and draw defensible conclusions from incomplete or uncertain evidence.
• Experience interpreting data from at least one structural or biophysical method, together with the ability to learn unfamiliar methods with support from specialists and advisers.
• Clear written and verbal communication, together with the independence to lead complex research while collaborating across scientific and technical disciplines.

• Experience with molecular glues, targeted protein degradation, induced proximity or other protein-protein interaction problems.
• Experience combining multiple sources of sparse or uncertain evidence through integrative structural modelling.
• Familiarity with machine-learning approaches to protein structure prediction, complex modelling or molecular design.
• Experience running scientific workflows across large datasets or shared computing infrastructure.
• Experience in biotechnology, drug discovery, E3 ligase biology, chemoproteomics or plant biology.
• Experience using AI-assisted development tools while validating their outputs and applying…