Research Engineer, Machine Learning

We're partnering with one of Europe's most exciting AI-native startups that's building an autonomous AI platform to fundamentally reinvent how new materials are discovered.Rather than relying on simulations alone, they're combining cutting-edge machine learning with a high-throughput experimental laboratory, creating a closed-loop system where AI designs new materials, experiments validate them, and real-world results continuously improve the models.Backed by $60M from leading global investors, they've assembled an exceptional team spanning AI, physics, materials science and engineering, and are now looking for outstanding Machine Learning Research Engineers to help build the infrastructure powering the next generation of AI for Science.You'll be working on:Building scalable ML infrastructure for frontier AI researchTranslating novel research into production-quality ML systemsDistributed training and inference across large GPU clustersOptimising model performance, training pipelines and experimentation workflowsDeveloping multimodal data pipelines spanning simulations, laboratory data and scientific literatureWorking alongside world-class AI researchers, engineers and scientists to deploy models into a real-world autonomous experimentation platformWe're looking for:Strong Machine Learning Engineering experienceDeep knowledge of modern ML architectures (Transformers, GNNs, Diffusion Models, etc.)Excellent Python skills with PyTorch and/or JAXExperience building scalable production ML systemsSomeone who enjoys turning cutting-edge research into robust, high-performance softwareBonus experience:Distributed GPU training (multi-GPU / multi-node)Scientific machine learning or simulation environmentsPerformance optimisation and systems engineeringDocker, Kubernetes, GCP or similar infrastructureScientific computing or research engineering backgroundsWhy this opportunity?This is a chance to join an exceptionally well-funded AI-for-Science company at an early stage and help build technology capable of accelerating scientific discovery in areas that matter globally. You'll work alongside some of the world's leading researchers on genuinely novel problems, with significant technical ownership from day one.