Applied Machine Learning​/Scientist

Location: London / Flexible (Hybrid or Remote)

Contract: Full-time

We are a technology-focused company applying modern machine-learning techniques to problems in molecular science and early-stage drug discovery. Our goal is to translate recent advances in ML into practical tools that support scientific research and decision-making in real-world discovery programmes.

The team brings together experience in machine learning, software engineering, and the life sciences, and collaborates closely with internal scientific teams and external research partners.

This role focuses on applying machine-learning methods to scientific and molecular datasets, with an emphasis on building robust, usable solutions rather than purely exploratory research. You will work across the full lifecycle of applied modelling projects, from understanding scientific objectives through to model development, evaluation, and integration into downstream workflows.

You will collaborate with researchers and engineers across multiple disciplines, contributing both technical expertise and practical insight into how ML systems perform in applied scientific settings.

• Apply machine-learning techniques to problems in molecular modelling and computational life sciences
• Design and execute modelling experiments, including dataset preparation, model training, fine-tuning, and evaluation
• Translate scientific questions into well-defined modelling approaches
• Contribute to the development of maintainable, reproducible ML codebases
• Work closely with scientists, engineers, and other stakeholders to support research objectives
• Assess model performance and identify areas for improvement in applied settings

• Hands-on experience applying machine-learning methods to real-world problems
• Experience working with biological, chemical, or scientific data
• Strong proficiency in Python and common ML frameworks (e.g. PyTorch)
• Experience writing and maintaining production-quality or research-grade ML code
• Experience working in interdisciplinary teams
• Exposure to collaborative projects with external partners
• Background in applied or translational ML research

• Opportunity to work on applied ML problems in a scientific discovery context
• High degree of ownership over technical work
• Collaboration with a multidisciplinary technical and scientific team
• Competitive compensation package, including equity participation