Healx - Software Engineer (Agentic Systems)
Requirements
• We'd love to hear from you if • You've built and shipped LLM-agentic systems that deliver real value — agents that use tools, orchestrate multi-step workflows and behave reliably in production. • You have at least 2 years of software engineer or ML engineer working experience. And you have strong software engineering fundamentals — you write clear, tested, maintainable Python code that others can build on • Fluency with the modern LLM/agent toolkit — model APIs, prompting, tool use, RAG, and the patterns this fast-moving ecosystem is converging on (MCP, agent frameworks, evals). • You enjoy working closely with non-engineers — you are the kind of person who'll sit with a scientist to understand what they actually need. • Experience in drug discovery, biology, or another life science domain • Familiarity with knowledge graphs or reasoning over structured or heterogeneous data • Experience building evaluation harnesses or testing strategies for LLM systems • A track record of picking up unfamiliar domains quickly and becoming useful fast • Interest in or experience with biotech / techbio and its impact on patient outcomes. • Working at Healx • Healx works from a modern accessible office in the centre of Cambridge within easy reach of the train station. We offer a flexible, diverse and inclusive working environment that considers your individual needs and believes in maintaining a sustainable work-life balance and we are open to discussing flexible working arrangements. • We are a hybrid team operating on a highly collaborative model that values synchronization and pair programming. • You will be welcomed by a team of colleagues with decades of accumulated experience in their areas of expertise, happy to help you develop your own skills in a highly collaborative environment and who are keen to provide guidance and support in your personal and career development plans. • Healx will provide you with support and guidance to help you do your best work and make an impact. We offer flexible working and believe in maintaining a sustainable work-life balance. If you require any reasonable adjustments during the recruitment process, please let us know — we’re happy to accommodate. • What’s on offer: • Financial – Competitive salary, share options, 7% employer pension contributions, life insurance of 4x base salary. • Health and Wellbeing – Private medical insurance, 25 days annual leave (plus bank holidays) with the option to purchase additional days to support a healthy work-life balance, wellbeing support via Spill and our Employee Assistance Programme. • Hybrid Working – We will only consider UK-based applicants for this position. We offer flexible and remote working options, home office set-up allowance, periodic in-person team days for company-wide collaboration and celebration. • Family Friendly – Enhanced family leave policies, miscarriage and fertility leave, flexible working practices. • Personal Development and Growth – Personal learning and development budgets, regular personal development conversations and career support.
Responsibilities
• Build and refine AI-agentic workflows on our internal framework to help drug discovery scientists generate the best testable therapeutic hypotheses to progress. • Translate discovery problems into agent designs, working out where an agentic approach genuinely adds value, and where it doesn’t • Integrate agents with our knowledge graph, proprietary methods, scientific literature and other sources so they reason over the right evidence for each problem • Expand and maintain our existing GenAI tools so they keep pace with the team’s needs and a fast-moving ecosystem • Contribute to how we evaluate agentic workflows, helping shape sensible evals, testing and quality standards as the practice matures • Write clear, maintainable, well-documented code that others on the team can build on • What success looks like • Got to grips with our agentic framework and the discovery problems it serves, and made your first contributions land in the codebase • Built working relationships with the scientists and engineers you partner with, and started turning their feedback into concrete improvements • Taken at least one agentic workflow from idea to a production tool scientists use in our drug discovery pipeline — built on our framework, with sensible evaluation and documentation • Operating with real independence — owning agentic workflows end to end, proposing improvements.
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