Computational Polymer Chemistry Lead

About the role

You will be the first hire for our new Polymer AI lab, leading the computational chemistry track: DFT screening, machine-learned interatomic potentials (MLIPs), and the bridge between quantum chemistry and our wet-lab dataset. You report to the CTO and collaborate daily with the Head of Polymers.

What you'll do

• Screen monomer candidates via DFT to generate enriched representations (HOMO/LUMO, partial charges, reactivity, polarizability) that topological fingerprints miss
• Evaluate and benchmark public universal MLIPs for fit with our monomer chemistry
• Run MLIP fine-tuning experiments on pilot wet-lab data as it arrives from the synthesis team
• Build the computational data pipeline from DFT features to training-ready representations
• Co-design the active learning loop with the CTO: which candidates should the wet lab synthesize next, based on model uncertainty and DFT priors?
• Set up and maintain the simulation infrastructure on cloud compute
• Train an in-house MLIP, run MD simulations for virtual screening of block copolymers, and generate synthetic training data at scale

What you'll need

• PhD and/or 5+ years of experience in computational chemistry, materials science, chemical physics, or a closely related field, with a focus in soft matter physics / polymers
• Hands-on DFT experience (Gaussian, ORCA, VASP, or CP2K) with production-scale calculations
• Working knowledge of machine-learned interatomic potentials (MACE, NequIP, Allegro, SchNet, or equivalent)
• Python fluency and comfort with ML frameworks (PyTorch or JAX), including writing training loops
• Experience connecting computational predictions to experimental validation and collaborating with a wet lab
• Published research demonstrating independent scientific contribution

Nice to have

• Experience with polymer or soft-matter simulations (coarse-grained MD, SCFT, block copolymer phase behavior)
• Familiarity with active learning or Bayesian optimization for experiment selection
• Experience with molecular simulation engines (LAMMPS, GROMACS, OpenMM)
• Prior work at an industrial R&D lab where predictions needed to translate into real decisions

What they offer

• Employee Stock Options
• Flexible working hours
• Learning & Development Programme
• 30 days of vacation plus up to 5 all-company holidays
• Gym membership and subsidised lunch
• Regular team events