> Markdown version of [/jobs/ext/2841989-research-engineer-machine-learning](https://www.wearedevelopers.com/jobs/ext/2841989-research-engineer-machine-learning). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Research Engineer, Machine Learning - **Company:** Diffractive Labs - **Location:** London, UK - **Salary:** £74,755.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Computer Clusters, Computer Programming, Extract Transform Load (ETL), Distributed Computing Environment, Distributed Systems, Python (Programming Language), Machine Learning, Performance Tuning, Google Cloud, Pytorch, Delivery Pipeline, Deep Learning, Kubernetes, Information Technology, Machine Learning Operations, Data Pipelines, Docker - **Published:** September 11, 2026 - **Apply:** https://www.adzuna.co.uk/jobs/details/5878694155 ## About the Role * 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) ## Description 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. ## Related Videos - [Data Science, ML & AI in the Oil and Gas Industry at NDT Global - Dr. Katja Träumner](https://www.wearedevelopers.com/videos/1308-data-science-ml-ai-in-the-oil-and-gas-industry-at-ndt-global-dr-katja-traumner) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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