Research Engineer, Machine Learning

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 Do

• Translate 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., 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.

• 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.

Nice to Have

• 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)

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.