Software Engineer (LLM Engineering), London

Your Impact

As a member of our newly formed LLM Engineering team, you will architect, implement and own the systems that bring LLMs into the heart of our scientific and business processes. You’ll need to apply first-principles thinking and design to build robust, secure, and scalable infrastructure for generative AI, even when there’s no pre-existing blueprint. You’ll need to use your understanding of the internal mechanics of these models and the ecosystem around them to drive your technical decisions. You’ll balance user experience and solutions engineering with high-performance platform engineering, to ensure our AI tools are scalable and reliable. This is an exciting opportunity to contribute to putting LLMs at the center of scientific discovery at scale!

What you will do

As a member of the LLM Engineering team, you will work on a subset of the following:

• System Architecture: Architect LLM-integrated systems by incorporating scalability, security, and user experience considerations from the earliest stages of development.
• Operational Excellence: Enable telemetry, observability, governance and cost management. Build platform components and tools for routing and making optimal use of various models.
• LLM & Agentic Infrastructure: Implement advanced context management, tool-use (MCP), skill tooling, and RAG to enhance research workflows and help scale our agentic infrastructure.
• Evaluation Frameworks: Build rigorous testing for probabilistic systems to ensure model reliability and safety. Build and maintain evals for different tasks.
• Agentic Tooling: Develop the internal frameworks and IDE integrations to leverage agentic coding and agentic workflows at scale. Contribute to shaping best practices and providing a great user experience.
• Refinement & Training: Support fine-tuning and RL efforts in collaboration with AI researchers to optimise models for complex biological and chemical data.
• Strategic Collaboration and Solutioning: Partner with researchers to translate scientific needs into technical AI specifications. Partner with other parts of the business to unlock LLM-based use-cases.

Skills and qualifications

Essential

• Software Engineering: Strong coding skills (Python) with a focus on production-grade, maintainable systems.
• System Design: Ability to architect and maintain (IaC) complex systems across multiple verticals (Security, UX, and Scalability) in the cloud.
• LLM Internals: Deep understanding of how models are trained, how they work internally, and their inherent limitations.
• LLM Serving stack: Experience with the LLM serving stack (e.g. vLLM) for open weight models for bringing the latest models to internal users.
• ML Literacy: Experience evaluating probabilistic ML systems and managing model "tool use", contexts and loops.
• First-Principles Thinking: A hype-detached approach to solving problems and selecting the right tech stack.
• Communication: Excellent stakeholder management and the ability to explain technical risks to non-experts.

Nice to have

• Applied LLM Experience: Prior success building and scaling systems centered around Large Language Models.
• ML Infrastructure: Experience setting up the underlying hardware or orchestration for ML systems.
• GCP: Prior experience building and deploying systems on Google Cloud (GCP).
• Scientific Domain: Interest or experience in biology, chemistry, or drug discovery; an interest in applying AI to scientific discovery.
• Fast-Moving Tooling: Familiarity with the latest in agentic tooling and developer frameworks.