Software Engineer (LLM Engineering), London

Location: London, United Kingdom; hybrid working with attendance in the office 3 days a week

Company

hirify.global applies frontier AI and machine learning to drug discovery, digital biology, and the development of new medicines.

What you will do

• Architect and implement LLM-integrated systems with strong scalability, security, and user experience foundations.
• Build platform components for telemetry, observability, governance, cost management, model routing, and efficient model use.
• Develop context management, tool-use through MCP, skill tooling, RAG, and agentic infrastructure for research workflows.
• Build evaluation frameworks and maintain rigorous tests for the reliability and safety of probabilistic systems.
• Develop internal frameworks and IDE integrations for agentic coding and agentic workflows.
• Support model fine-tuning and reinforcement learning efforts with AI researchers, and translate scientific needs into technical AI specifications.

Requirements

• Strong Python coding skills focused on production-grade, maintainable systems.
• Ability to architect and maintain complex cloud systems using infrastructure as code across security, user experience, and scalability concerns.
• Deep understanding of LLM training, internal operation, and limitations.
• Experience with LLM serving stacks such as vLLM for open-weight models.
• Experience evaluating probabilistic machine learning systems and managing model tool use, contexts, and loops.
• Excellent stakeholder management and ability to explain technical risks to non-experts.

Nice to have

• Experience building and scaling systems centered around large language models.
• Experience with ML hardware, orchestration, or infrastructure.
• Experience building and deploying systems on Google Cloud Platform.
• Interest or experience in biology, chemistry, drug discovery, or AI for scientific discovery.
• Familiarity with current agentic tooling and developer frameworks.

Culture & Benefits

• Collaborative, interdisciplinary environment spanning drug discovery and machine learning.
• Values centered on thoughtful, brave, determined, and connected work.
• Shared learning and support for employees with diverse experiences and perspectives.
• Hybrid working model designed to support in-person knowledge sharing and collaboration.
• Workplace accommodations are available for candidates with additional needs.