Teams at Diffractive Labs

Build and own cloud infrastructure and GPU compute environments for ML model training and deployment. Implement IaC, CI/CD, orchestration, containerisation, monitoring, security, and internal tooling to enable reproducible, scalable research-to-production ML workflows.

Develop, train, and fine-tune machine-learned interatomic potentials (MLIPs) for magnetic and structural materials. Design DFT dataset workflows, active learning loops, and extend MLIP architectures to capture spin-lattice interactions. Build reproducible model and dataset pipelines and collaborate with materials scientists to translate physical intuition into training objectives.

Operate and maintain research-grade synthesis and characterisation instruments (XRD, SEM, vacuum systems, high-field magnets, high-temperature metallurgy). Run reproducible experiments, perform preventive maintenance and fault diagnosis, write SOPs and risk assessments, manage consumables and inventory, collaborate with materials scientists and ML researchers, and support integration of experimental data into digital/AI pipelines.