Biotechnology | Metals | Molecular Modelling
The Opportunity
An early‑stage biotechnology company is seeking a Senior Computational Chemist to take a strategically important, hands‑on role developing high‑throughput docking and molecular dynamics workflows for metal–peptide and metal–protein systems, including non‑standard and f‑block elements. This role sits at the intersection of computational chemistry, machine‑learning forcefield development, and experimental decision‑making, with direct influence over pipeline design, experimental prioritisation, and IP generation. You will work closely with a wet‑lab team and senior stakeholders, helping shape a novel computational capability from the ground up.
What You’ll Be Doing
* Designing, building, and automating high‑throughput docking pipelines for peptide‑ and protein‑metal binding
* Running and optimising molecular dynamics simulations to evaluate binding geometry, stability, and selectivity
* Integrating docking, MD, QM, DFT and (over time) QM/MM approaches to define selection rules and go / no‑go criteria
* Developing and integrating custom machine‑learning force fields and parameterisation methods for non‑standard metals
* Scaling and automating workflows on HPC and GPU environments
* Translating modelling outputs into actionable guidance for wet‑lab experiments and programme strategy
* Contributing to a fast‑moving, early‑stage technical environment with both scientific and strategic input
Required Experience & Skills
* Strong hands‑on experience delivering molecular docking and MD workflows in production or research environments
* Demonstrated experience modelling or parameterising metal coordination in biomolecular systems
* Exposure to non‑standard metals, including transition metals and/or f‑block elements
* Experience with MD engines such as GROMACS, AMBER, OpenMM, NAMD, or CHARMM
* Proficiency in Python and/or Bash for scientific automation
* Comfortable working on HPC systems, GPUs, and containerised environments
* Experience with QM or QM/MM methods (DFT and parameter development expected by end of year one)
* Familiarity with enhanced sampling or free energy methods
* Experience in early‑stage biotech, startup, or fast‑moving research environments
* Prior work developing or implementing machine‑learning force fields or learned potentials
* Strong communication skills, including explaining complex modelling outcomes to non‑specialist stakeholders
Desirable (but Not Essential)
* Peptide or protein design experience
* Development of automated tools or pipelines used by non‑specialists
Background
PhD in Computational Chemistry, Computational Biology, or similar with 3+ years relevant industry experience or MSc in a relevant discipline with 5+ years industry experience
Working Pattern
Full time – 32 hours per week, Monday to Friday
Core hours: 09:30–16:00
Designed to support sustainable, accessible working and strong work–life balance.
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