Senior Computational Chemist, LNP Design; Part-time

Cure Age Therapeutics is on a mission to cure peripheral nerve diseases, starting with Neurofibromatosis type 1. We are looking for a senior computational chemist to lead the rational, computationally guided design of our lipid nanoparticle delivery platform. Nucleic acid delivery is the central challenge in our field, and we believe the next generation of LNPs will be designed rather than screened.

This role combines chemical reasoning with AI-enabled chemistry and cheminformatics tools to decide what we make next. This is a great opportunity to shape the scientific strategy of a growing start-up from the ground up, working directly with the founding team. The role is open to part-time or flexible arrangements for the right candidate.

• Apply chemical and biophysical principles to optimise LNP composition and formulation parameters for nucleic acid delivery.
• Drive the rational design of targeted LNPs, exploring strategies to enhance delivery to specific cell types and tissues.
• Leverage AI-enabled chemistry tools, cheminformatics platforms, predictive models, and public databases to identify and prioritise promising formulation components and chemical modifications.
• Apply molecular modelling and simulation approaches where appropriate to inform design decisions.
• Collaborate closely with the experimental team to translate computational insights into testable strategies and efficient screening plans, using Design of Experiments (DoE) principles where appropriate.
• Analyse experimental results and iteratively refine formulation design.

• MSc or PhD in Chemistry, Medicinal Chemistry, Computational Chemistry, Pharmaceutical Sciences, Biophysics, or a related discipline, with relevant experience.
• Strong understanding of chemical structure–property relationships, molecular interactions, and physicochemical principles.
• Experience with LNPs, lipid-based drug delivery, or related formulation technologies.
• Demonstrated ability to apply computational chemistry, cheminformatics, or AI-assisted molecular design tools to practical scientific challenges.
• Ability to integrate computational predictions, chemical reasoning, and experimental evidence into actionable recommendations.
• Self-driven, analytical, and comfortable taking ownership in a small, fast-moving team.

• Experience with targeted delivery systems or ligand optimisation.
• Familiarity with computational approaches to assessing immunogenicity, toxicity, and other molecular liabilities.
• Experience with molecular modelling or molecular dynamics simulations.
• Familiarity with DoE or other systematic formulation optimisation approaches.