We are seeking Research Engineers, with strong machine learning experience, to help build the infrastructure, tools, and prototypes that power our AI-driven material discovery engine. You will work across research and engineering, turning new ideas in modelling, reasoning, and experiment automation into robust, scalable systems.
You will be joining a small, highly ambitious team of world-renowned engineers, AI researchers, and materials scientists. We move fast and value people who are energised by that.
This is a role for someone who is excited about ML at scale, enjoys turning research ideas into working code, and wants to make a meaningful contribution to material science.
What You'll DoTranslate cutting-edge ML research and novel architectures into highly performant, scalable implementations for our autonomous discovery platform.
Design, build, and optimize large-scale distributed training pipelines and inference systems on GPU clusters.
Profile and optimize model code, identifying and resolving bottlenecks in compute, memory, and data loading to dramatically accelerate our research iteration cycles.
Develop robust evaluation frameworks and experiment-tracking tooling to bridge the gap between computational model predictions and real-world, physical lab results.
Curate and architect data pipelines for complex, multimodal scientific data (simulations, structured lab outputs, unstructured text) to feed our training loops.
Work tightly alongside AI researchers, materials scientists, and software engineers to ensure our models aren't just theoretically sound, but practically deployable in a closed-loop hardware environment.
Master's or equivalent experience in Computer Science, Engineering, or a closely related field.
Deep understanding of machine learning principles and techniques and modern model architectures (e.g. GNNs, Diffusion Models, Transformers)
Proven hands-on experience building production ML systems, with a clear understanding of training infrastructure, distributed systems, and deployment workflows.
Strong experience with deep learning frameworks such as PyTorch or JAX Strong programming skills in Python and familiarity with PyTorch or an equivalent ML framework.
Comfortable taking research ideas (papers, prototypes) and turning them into working, tested code.
Experience with large-scale or distributed training and performance optimisation on GPU clusters (multi-GPU/multi-node).
Experience applying ML systems in a scientific, simulation, or research computing setting.
Familiarity with scientific data formats and reproducibility practices.
Experience with technical infrastructure and low-level engineering (e.g. GCP, Kubernetes, Docker)
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.
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.
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!
