Senior Computational Chemist (AI)

Location: Cambridge, UK; hybrid-friendly with purposeful in-person collaboration at the Babraham Research Campus.

Company

hirify.global is a global biopharmaceutical company developing treatments for patients with serious and rare diseases.

What you will do

• Provide senior technical expertise and hands-on computational chemistry support across small-molecule discovery programs from hit identification through late lead optimisation.
• Develop and test scientific hypotheses that influence compound design, project strategy, potency, selectivity, and developability.
• Apply structure-based and physics-based methods, including docking, pose prediction, molecular dynamics, free-energy calculations, quantum mechanical methods, and water thermodynamic analysis.
• Use cheminformatics and machine learning for QSAR, ADMET modelling, chemical-space analysis, and analogue enumeration.
• Develop computational workflows, evaluate emerging methods, and strengthen the Cheminformatics team's capabilities.
• Collaborate with medicinal chemists, structural biologists, DMPK scientists, and compound profiling teams while mentoring colleagues and presenting complex analyses.

Requirements

• PhD or MSc with equivalent industrial experience in computational chemistry, chemistry, chemical physics, biophysics, or a related discipline.
• Typically 8+ years of industrial experience after a PhD applying computational chemistry to small-molecule drug discovery.
• Experience supporting multiple programs across hit identification and lead optimisation.
• Deep expertise in physics-based computational chemistry, medicinal chemistry, structure-based drug design, predictive modelling, and multi-parameter optimisation.
• Experience developing computational strategies that influence lead development decisions.
• Excellent communication skills and the ability to influence multidisciplinary and senior teams through technical expertise.

Nice to have

• Experience integrating physics-based methods with cheminformatics, machine learning, or AI-driven approaches.
• Experience with Schrödinger or equivalent molecular modelling platforms, OpenMM, KNIME, Linux, GPU, HPC, or cloud infrastructure.
• Track record of developing reusable computational workflows, publishing or presenting scientific innovation, and informally mentoring scientists.

Culture & Benefits

• Flexible work schedules with purposeful in-person collaboration.
• Competitive retirement benefits, global equity awards, and participation in the Employee Stock Purchase Plan.
• Career development through internal learning, external programs, LinkedIn Learning, and role-specific training.
• AI-powered and on-demand learning tools with hybrid-friendly technology and IT support.
• Global recognition programs and Employee Resource Groups.