Lead Computational Chemist - Polymer AI

We’re currently supporting a highly innovative, venture-backed advanced materials company at the intersection of AI/ML, computational chemistry, and next-generation polymer engineering.

The business is building a proprietary molecular intelligence platform focused on designing entirely novel high-performance materials through a combination of machine learning, simulation, and experimental science.

This is a unique opportunity to become the founding computational hire within a newly created Polymer AI function, helping shape a long-term vision around AI-native materials discovery and foundational polymer datasets.

The Role:

You’ll lead the computational chemistry and modelling track, working across:

• DFT & quantum chemistry workflows

• Machine-learned interatomic potentials (MLIPs)

• Active learning systems

• Polymer & soft matter simulations

• AI-enabled materials discovery infrastructure

Working closely with senior technical leadership and experimental scientists, your work will directly influence how computational and wet-lab systems integrate to accelerate materials development.

Key Responsibilities:

• Build scalable computational pipelines for molecular and polymer modelling

• Evaluate and fine-tune MLIPs for novel chemistry applications

• Develop simulation and virtual screening workflows

• Support active learning strategies for experimental prioritisation

• Collaborate closely with synthesis and experimental teams

• Help establish long-term infrastructure for AI-driven polymer discovery

Required Background:

• PhD and/or industry experience in Computational Chemistry, Materials Science, Chemical Physics, or related disciplines

• Hands-on experience with DFT platforms such as ORCA, Gaussian, VASP, or CP2K

• Exposure to MLIPs including MACE, NequIP, Allegro, SchNet or related approaches

• Strong Python skills with PyTorch and/or JAX

• Experience translating computational outputs into experimentally useful insights

Why Consider It?

• Opportunity to shape a next-generation AI-for-materials platform from an early stage

• Deeply technical, research-led environment

• Significant ownership and influence

• Equity participation available

• Berlin-based with relocation support

Reach out to discuss: jack@qpexec.com