Machine Learning Researcher

About the company We are a London-based, early-stage AI lab building AI systems that help scientists learn from real-world experimentation and accelerate the path to commercially valuable materials. Our mission is to combine machine learning, automation and deep domain expertise to solve hard scientific problems with real-world impact. We are a small, ambitious team bringing together strong ML, engineering and materials science capability. We value pace, rigour, curiosity and low ego, and we want people who are energised by working on focused problems where their contribution is visible from day one. We offer a flexible hybrid working model, regular collaboration in London, and an inclusive environment where people from all backgrounds are welcomed and supported. What the job entails As a Research Scientist, Machine Learning, you will own research problems at the intersection of modelling, reasoning and experiment automation. Your work will help power an AI-driven discovery engine by improving how the system learns from sparse, noisy and high-dimensional scientific data. You will design and prototype novel ML approaches, develop active learning and optimisation methods that decide which experiments should run next, and work closely with materials scientists and chemists to translate scientific constraints into rigorous mathematical models and loss functions. You will take ideas from theory through to proof-of-concept, establish strong baselines, and partner with engineers to scale successful approaches into production. The role is hands-on and requires both research depth and production-quality coding. Key requirements / What we look for * PhD in machine learning, computer science, physics or a related field; [suggestion: 2+ years’ post-PhD or equivalent industry experience] * Strong track record in novel ML research and evaluation * Strong Python skills; PyTorch or equivalent, plus production-quality code * Experience with Linux, Git and HPC/cloud platforms * [suggestion: experience in scientific/simulation domains, and evidence of impact through publications, open source or applied research] Compensation \& Benefits * Competitive base salary * Generous equity, with performance- and scope-based additional awards * Benefits package