What We’re Looking For
We are looking for a Machine Learning Engineer to take ownership of training and fine-tuning machine-learning interatomic potentials (MLIPs) for magnetic and structural materials. You will work at the intersection of modern ML and first-principles simulation, leveraging our DFT datasets to feed our models and pushing MLIP architectures into new physical regimes; particularly spin-dependent interactions.
You will be joining a small, highly ambitious team of world-class materials scientists, engineers, and AI researchers. We move fast and value people who are energised by that.
This is a role for someone who has a deep understanding of MLIPs, is excited about pushing the boundaries of machine learning, and make a meaningful contribution to material science.
What You’ll Do
Pre-train and fine-tune MLIPs (MACE, CHGNet, Orb, or equivalent) for solid-state systems, with a focus on magnetic materials.
Design and build DFT training-set workflows, including active learning loops, convergence testing, and data curation.
Extend existing MLIP architectures to capture spin-lattice interactions.
Build and maintain automated, reproducible workflows for dataset generation and model iteration using tools such as AiiDA, FireWorks, or equivalent frameworks.
Work directly with materials scientists to translate physical intuition about magnetism into training objectives and dataset design decisions.
Skills & Qualifications
PhD in physics, chemistry, materials science, or a closely related field; solid-state focus strongly preferred.
Proven hands-on experience training or fine-tuning MLIPs, with a clear understanding of training dynamics, loss landscapes, and generalisation behaviour.
Experience working with DFT-generated training sets and experimental material science data; understanding what makes a dataset sufficient or deficient for a given system, and being able to work with our DFT team or our experimental scientists to diagnose and close gaps.
Strong Python skills and production-quality research code; experience with PyTorch or JAX; ideally also C/C++ or Rust.
Familiarity with atomistic simulation packages (VASP, Quantum Espresso, LAMMPS, or similar).
Evidence of significant research impact through publications in ML for atomistic modelling, computational materials science, or related technical disciplines.
Nice to Have
Background in long-range or equivariant message-passing architectures for extended systems.
Experience with spin-polarised or non-collinear DFT calculations.
Contributions to open-source atomistic simulation or ML packages.
Experience with automated workflow frameworks such as AiiDA or Fireworks.
Why Join Us
Work on one of the most technically demanding open problems in ML for materials.
Collaborate directly with world-class physicists, experimentalists, and ML researchers in a single team.
Diffractive is building the AI Material Scientist that autonomously learns from real-world experimentation to push the boundaries of scientific discovery. We're early, moving fast, and working on problems that genuinely matter.
You'll join a small, high-calibre team where your work has real impact from day one. We're London-based with a flexible approach to how and where you work. We offer competitive salary, generous equity and benefits. You'll have a real stake in what you build and in the company's overall success.
How to Apply
If you're excited about this role and believe you could thrive in it, we'd encourage you to apply even if you may not align with every part of the job description.
Diffractive is an equal opportunities employer. We are committed to creating an inclusive environment for all employees and welcome applications from people of all backgrounds, experiences, and identities.
If you require any adjustments or accommodations at any point during the interview process please let us know - we will be happy to help.
Hit the apply button below to submit your application. We are looking forward to hearing from you!
