<div class="show-more-less-html__markup show-more-less-html__markup--clamp-after-5 relative overflow-hidden"> <p>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.</p><p><br/></p><p>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.</p><p><br/></p><p>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.</p><p><br/></p><p><br/></p><p><strong>The Role:</strong></p><p><br/></p><p>You’ll lead the computational chemistry and modelling track, working across:</p><p><br/></p><p>• DFT &amp; quantum chemistry workflows</p><p>• Machine-learned interatomic potentials (MLIPs)</p><p>• Active learning systems</p><p>• Polymer &amp; soft matter simulations</p><p>• AI-enabled materials discovery infrastructure</p><p><br/></p><p>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.</p><p><br/></p><p><br/></p><p><strong>Key Responsibilities:</strong></p><p><br/></p><p>• Build scalable computational pipelines for molecular and polymer modelling</p><p>• Evaluate and fine-tune MLIPs for novel chemistry applications</p><p>• Develop simulation and virtual screening workflows</p><p>• Support active learning strategies for experimental prioritisation</p><p>• Collaborate closely with synthesis and experimental teams</p><p>• Help establish long-term infrastructure for AI-driven polymer discovery</p><p><br/></p><p><br/></p><p><strong>Required Background:</strong></p><p>• PhD and/or industry experience in Computational Chemistry, Materials Science, Chemical Physics, or related disciplines</p><p>• Hands-on experience with DFT platforms such as ORCA, Gaussian, VASP, or CP2K</p><p>• Exposure to MLIPs including MACE, NequIP, Allegro, SchNet or related approaches</p><p>• Strong Python skills with PyTorch and/or JAX</p><p>• Experience translating computational outputs into experimentally useful insights</p><p><br/></p><p><br/></p><p><strong>Why Consider It?</strong></p><p>• Opportunity to shape a next-generation AI-for-materials platform from an early stage</p><p>• Deeply technical, research-led environment</p><p>• Significant ownership and influence</p><p>• Equity participation available</p><p>• Berlin-based with relocation support</p><p><br/></p><p><br/></p><p>Reach out to discuss: jack@qpexec.com</p> </div>