Jobs 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.

Implement and scale cutting-edge ML research into production-ready systems: build distributed training and inference on GPU clusters, optimize model performance, create evaluation and experiment-tracking tooling, architect multimodal scientific data pipelines, and collaborate with researchers and materials scientists to deploy models in closed-loop hardware environments.

Build and maintain software and orchestration that turns a physical lab into an autonomous system: implement instrument drivers, coordinate robots and data pipelines, automate workflows, integrate new instruments, and ensure reliability, error handling, and reproducibility while collaborating with materials scientists and ML researchers.

Lead ML research for an AI-driven materials discovery platform: design and prototype architectures (GNNs, generative, foundation models), develop active learning/optimization strategies, address representation learning for sparse noisy experimental data, collaborate with materials scientists to encode physics into models, and drive proofs-of-concept to production with engineering partners.