About the role
About Xaira Therapeutics Xaira is an innovative biotech startup focused on leveraging AI to transform drug discovery and development. The company is leading the development of generative AI models to design protein and antibody therapeutics, enabling the creation of medicines against historically hard-to-drug molecular targets. It is also developing foundation models for biology and disease to enable better target elucidation and patient stratification. Collectively, these technologies aim to continually enable the identification of novel therapies and to improve success in drug development. Xaira is headquartered in the San Francisco Bay Area, Seattle, and London.
About
the Role Xaira is pioneering an AI-native approach to discovering and developing the next generation of life-changing medicines. We are building predictive and agentic AI systems across drug discovery, and the X-Scientist team accelerates this mission by making AI-assisted science reliable, usable, and scalable across drug discovery workflows. On the X-Scientist team, you will work at the intersection of AI, software engineering, and therapeutic discovery. You will collaborate closely with AI scientists, engineers, platform teams, and drug discovery experts to build systems that connect AI models, biological tools, datasets, and scientific workflows. This role spans backend systems, APIs, tool-calling infrastructure, workflow orchestration, developer-facing SDKs and CLIs, and agentic interfaces that help scientists run analyses, inspect intermediate steps, and iterate on results.
What You'll Do Backend systems and APIs that connect AI models to scientific tools, datasets, and analysis workflows Tool-calling infrastructure, CLIs, SDKs, and workflow runners that scientists and ML researchers use day-to-day Agentic interfaces where users can run analyses, inspect intermediate steps, and iterate on results Observability and testing for long-running workflows that call external tools and produce scientific outputs Design reliable systems for long-running, multi-step workflows with strong testing, observability, logging, and maintainability. Collaborate with scientific and technical teams to turn complex research workflows into usable, extensible software.
