Software Engineer (LLM Engineering)

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. 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., 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. Isomorphic Labs (IsoLabs) was launched in 2021 to advance human health by building on and beyond the Nobel-winning AlphaFold system. Since then, our interdisciplinary team of drug discovery experts and machine learning specialists has built powerful new predictive and generative AI models that accelerate scientific discovery at digital speed.