Computational Polymer Chemistry Lead

About This RoleAI processing…

Key Responsibilities

• 1
  Screen monomer candidates via DFT to generate enriched representations (HOMO/LUMO, partial charges, reactivity, polarizability) that topological fingerprints miss
• 2
  Evaluate and benchmark public universal MLIPs for fit with our monomer chemistry
• 3
  Run MLIP fine-tuning experiments on pilot wet-lab data as it arrives from the synthesis team
• 4
  Build the computational data pipeline from DFT features to training-ready representations
• 5
  Co-design the active learning loop with the CTO: which candidates should the wet lab synthesize next, based on model uncertainty and DFT priors?
• 6
  Set up and maintain the simulation infrastructure on cloud compute
• 7
  As the dataset grows: train an in-house MLIP, run MD simulations for virtual screening of block copolymers, and generate synthetic training data at scale
• 8
  Manage the compute budget and make the cloud-vs-on-prem recommendation as we scale

Requirements

• 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). You've trained and evaluated MLIPs on real systems
• Python fluency and comfort with ML frameworks (PyTorch or JAX), including writing training loops
• Experience connecting computational predictions to experimental validation. You've collaborated with a wet lab and understand what makes a prediction useful in practice
• Published research demonstrating independent scientific contribution
• Experience with polymer or soft-matter simulations (coarse-grained MD, SCFT, block copolymer phase behavior, polymer thermodynamics)
• Familiarity with active learning or Bayesian optimization for experiment selection
• Experience with molecular simulation engines (LAMMPS, GROMACS, OpenMM, or equivalent)
• Background in concept bottleneck models, physics-informed ML, or interpretable ML for materials
• Contributions to open-source computational chemistry or ML tooling
• Prior work at an industrial R&D lab, where predictions needed to translate into real decisions

Perks & Benefits