AI Engineer (Agentic Systems)
First seen by Alion on Aug 27, 2026.
Do you want to use your engineering skills to build agentic workflows that help find treatments for rare disease patients?
About Healx
Healx is an AI-powered tech bio company that is redesigning drug discovery. With 10,000 rare diseases affecting 400 million people globally, 90% of which have no approved treatment, Healx is on a mission to pioneer the next generation of drug discovery to help rare disease patients in need. We combine data, artificial intelligence and deep pharmacology expertise to develop treatments more quickly and cheaply than traditional drug discovery.
Diversity and inclusion sits at the heart of our mission to help people with rare diseases, and we believe that attracting and empowering a diverse team is critical to achieving this goal. We welcome applications from people from all backgrounds and walks of life.
Below we have included the qualities that we feel are required for you to excel in this role; however we appreciate that people can apply transferable skills and experience. If you think you have what it takes, love our mission and resonate with our values but are worried you don't tick every box - we still want to hear from you and encourage you to apply!
Our values
Care for Rare - Rare disease patients are at the heart of what we do
Grow as individuals - We are learners always seeking to enhance our expertise
Win as a team - We strive to remain inclusive and diverse and we celebrate successes and lessons together
Innovate and deliver - Our mission requires rapid innovation and calculated risks that won’t compromise our high standards
The role
Healx is looking for an Agentic AI Engineer to build LLM-agentic workflows that power our drug discovery platform.
Reporting to our Director of Tech Strategy, you'll design and build agents that solve real drug discovery problems. Working closely with scientists across the company, you'll understand the questions that matter most to their work, then design and refine agents that reason over our knowledge graph, proprietary methods, scientific literature and other sources to generate, triage and rationalise testable therapeutic hypotheses for rare diseases.
You won't be starting from scratch. We've adopted an agentic framework (our stack currently includes Python, ADK and AgentSpace) and built workflows already delivering impact across our drug discovery platform. But this is a fast-moving field, so a big part of the role is helping us adapt as the landscape shifts. You'll work alongside the technical lead who owns our agentic infrastructure, with scope to take ownership of specific workflows and grow your influence as you do.
You'll join a cross-functional team of machine learning engineers, software engineers, bioinformaticians, drug discovery scientists and clinicians, all working to sharpen how Healx makes better decisions in drug discovery.
Do you want to use your engineering skills to build agentic workflows that help find treatments for rare disease patients?
About Healx
Healx is an AI-powered tech bio company that is redesigning drug discovery. With 10,000 rare diseases affecting 400 million people globally, 90% of which have no approved treatment, Healx is on a mission to pioneer the next generation of drug discovery to help rare disease patients in need. We combine data, artificial intelligence and deep pharmacology expertise to develop treatments more quickly and cheaply than traditional drug discovery.
Diversity and inclusion sits at the heart of our mission to help people with rare diseases, and we believe that attracting and empowering a diverse team is critical to achieving this goal. We welcome applications from people from all backgrounds and walks of life.
Below we have included the qualities that we feel are required for you to excel in this role; however we appreciate that people can apply transferable skills and experience. If you think you have what it takes, love our mission and resonate with our values but are worried you don't tick every box - we still want to hear from you and encourage you to apply!
Our values
Care for Rare - Rare disease patients are at the heart of what we do
Grow as individuals - We are learners always seeking to enhance our expertise
Win as a team - We strive to remain inclusive and diverse and we celebrate successes and lessons together
Innovate and deliver - Our mission requires rapid innovation and calculated risks that won’t compromise our high standards
The role
Healx is looking for an Agentic AI Engineer to build LLM-agentic workflows that power our drug discovery platform.
Reporting to our Director of Tech Strategy, you'll design and build agents that solve real drug discovery problems. Working closely with scientists across the company, you'll understand the questions that matter most to their work, then design and refine agents that reason over our knowledge graph, proprietary methods, scientific literature and other sources to generate, triage and rationalise testable therapeutic hypotheses for rare diseases.
You won't be starting from scratch. We've adopted an agentic framework (our stack currently includes Python, ADK and AgentSpace) and built workflows already delivering impact across our drug discovery platform. But this is a fast-moving field, so a big part of the role is helping us adapt as the landscape shifts. You'll work alongside the technical lead who owns our agentic infrastructure, with scope to take ownership of specific workflows and grow your influence as you do.
You'll join a cross-functional team of machine learning engineers, software engineers, bioinformaticians, drug discovery scientists and clinicians, all working to sharpen how Healx makes better decisions in drug discovery.
Key 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
In 3 months, you have:
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.
In 6 months you have:
What we are looking for
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.
It’s a bonus if you have: