About the job
About Xaira Therapeutics
Xaira Therapeutics is a pioneering biotechnology startup committed to harnessing the power of artificial intelligence to revolutionize the processes of drug discovery and development. The company is at the forefront of creating generative AI models designed to develop protein and antibody therapeutics, paving the way for effective treatments against previously challenging molecular targets. Additionally, Xaira is developing foundational models for biology and disease that facilitate enhanced target identification and patient stratification. Through these innovative technologies, Xaira aims to continuously uncover novel therapies and significantly enhance the success rates in drug development. With headquarters located in the vibrant San Francisco Bay Area, as well as offices in Seattle and London, Xaira is positioned to lead in the biotech field.
About the Role
We are on the lookout for a talented Biomedical AI Research Engineer who will design and implement sophisticated AI systems capable of reasoning over biomedical knowledge, orchestrating multi-step analyses, and autonomously integrating diverse scientific data sources to expedite therapeutic discovery.
This role transcends conventional LLM + RAG pipelines. You will be tasked with constructing AI agents that can effectively retrieve, synthesize, evaluate, and act upon biomedical information derived from literature, omics datasets, and clinical records, functioning as intelligent systems embedded within discovery workflows.
Key Responsibilities
- Create and execute agentic AI architectures that demonstrate multi-step reasoning, planning, and tool utilization within biomedical contexts.
- Develop LLM-based agents capable of dynamically retrieving, ranking, and synthesizing knowledge from scientific literature, knowledge graphs, omics datasets, and clinical data.
- Establish memory and feedback mechanisms that empower agents to refine hypotheses, update context, and adapt to changing data.
- Incorporate external tools (analysis pipelines, simulation engines, statistical models, databases) into autonomous agent workflows.
- Design scalable systems that merge structured and unstructured biomedical data within production-grade AI environments.
- Collaborate closely with computational biologists, translational scientists, and software engineers to integrate agentic systems into practical discovery pipelines.
- Assess robustness, reliability, and interpretability of autonomous systems in critical biomedical scenarios.
