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Salary: USD 140,000 - 215,000 / annual
Xaira Therapeutics is an AI-native biotech startup leveraging generative AI to transform drug discovery and development. The company builds AI models for protein and antibody design, foundation models for biology, and systems to enable better target identification and patient stratification.
The X-Scientist team accelerates this mission by making AI-assisted science reliable, usable, and scalable across drug discovery workflows. As a Software Engineer on this team, you will work at the intersection of AI, software engineering, and therapeutic discovery, collaborating with AI scientists, engineers, platform teams, and drug discovery experts.
You will build backend systems and APIs that connect AI models to scientific tools, datasets, and analysis workflows. Responsibilities include developing tool-calling infrastructure, CLIs, SDKs, and workflow runners that scientists and ML researchers use daily. You'll design agentic interfaces where users can run analyses, inspect intermediate steps, and iterate on results. You'll also implement observability and testing for long-running workflows that call external tools and produce scientific outputs, ensuring reliable systems with strong testing, logging, and maintainability.
Key qualifications include strong Python engineering fundamentals with clean, typed, tested, maintainable code. You should have production experience building at least one of: a library, CLI, API, SDK, backend service, workflow system, or developer platform. Familiarity with LLM application development, agent frameworks, tool calling, MCP-style interfaces, or orchestration systems is important. You should be comfortable with complex systems combining multiple components, external calls, domain-specific logic, and evolving user needs, with good instincts for abstraction, error handling, and reliability.
Nice-to-have qualifications include background or interest in computational biology, bioinformatics, protein design, single-cell genomics, or biomedical AI. Familiarity with biomedical databases and tools (PubMed, UniProt, AlphaFold, PDB, DepMap) is a plus. Curiosity about biology and scientific research is valued; while a biology background is not required, enthusiasm for learning the domain and collaborating with scientists and ML researchers is essential.
