ML Research Engineer, London

Overview

In this role you contribute to frontier research at the intersection of AI and drug design, joining a highly creative engineering team. You translate research ideas into robust AI models and scalable infrastructure, working with scientists and engineers to advance foundational models. You data-drive experiments, optimize models, and help bring AI-powered solutions toward production. You will help scale our model platforms and contribute to cutting-edge biopharmaceutical innovation. This is a mission-driven opportunity in a collaborative culture that values curiosity and impact.

Pay / Benefits

• hybrid working

Responsibilities

• Translate research concepts into practical implementations and maintain robust ML codebases and data pipelines
• Design and run experiments to evaluate model performance and robustness using state-of-the-art ML methods
• Implement algorithms to analyze and evaluate AI model performance and optimize inference
• Advise on productionization of AI/ML models and integration into products with ongoing monitoring
• Develop tooling and infrastructure to support research and deployment
• Collaborate with research scientists and engineers, participate in code reviews, and share knowledge
• Proactively address technical challenges and contribute to scaling foundation and applied model platforms
• Lead independent engineering projects aligned with research goals

Key requirements

• PhD in a technical subject with engineering component and AI/ML exposure, or Bachelor/Master with 2+ years of ML model development experience
• Strong software design and algorithms for deep learning frameworks
• Experience with modern ML frameworks (JAX, PyTorch or TensorFlow)
• Distributed systems and runtimes; familiarity with compilers (e.g., XLA, CUDA, Triton) and large-scale training/serving
• Experience navigating complex research codebases and building data processing pipelines
• Solid mathematical foundations (numerics, statistics, linear algebra) and understanding of ML theory
• Experience across the ML lifecycle from research to development

• collegial and collaborative mindset
• curiosity and creativity
• initiative and problem-solving
• JAX
• PyTorch
• TensorFlow

…