Senior Computational Protein Design & AI Scientist

You will evaluate, develop and utilize cutting‑edge computational design for miniproteins, VHHs (nanobodies), and antibodies, leveraging modern machine learning approaches including large language models (LLMs) and diffusion‑based generative models. This role sits at the interface of structural biology, computational modeling, and AI‑driven molecule design.

This position reports to the Director of Data Applications and Analytics at Abcam and will undertake projects for the Danaher Affinity Reagents Autonomous Laboratory and is based in Cambridge, UK.

In This Role, You Will Have The Opportunity To

• Lead computational design of miniproteins, VHHs, and antibodies.
• Apply and integrate LLM‑based and diffusion‑based generative models with structure‑ and physics‑based protein design methods.
• Collaborate closely with wet‑lab, to drive iterative design/make/test/learn cycles.
• Analyze structural, biophysical, and binding data to refine models and improve design quality.
• Contribute to strategy and communicate results to internal and external stakeholders.

The Essential Requirements Of The Job Include

• Expertise in AI/ML for molecular design, such as protein structural modelling, generative protein or antibody design, optimization of binding, developability, or stability profiles.
• Familiarity with immune repertoire sequencing, phage/yeast display, or experimental modalities used to assess and optimize biomolecule properties.
• Strong grounding in statistics, applied mathematics, and computational modelling.
• Fluency in Python, including rapid prototyping, production‑quality code, and use of relevant bioinformatics and ML libraries.
• Ability to navigate and solve complex, ambiguous scientific data problems with creativity and rigor.
• 5+ years of relevant experience in computational biology, bioinformatics, or AI for molecular design.

It would be a plus if you also possess previous experience in:

• Software engineering best practices and collaboration with engineering teams.
• MLOps or model lifecycle management.
• Working with databases, data integration strategies, and data modelling.
• Working in dynamic, multidisciplinary settings.
• Learning new tools and applying them to complex biological challenges.

#J-18808-Ljbffr…