Principal Scientist, Target Discovery & Portfolio Strategy

Xaira Therapeutics•South San Francisco, CA

2d•$164,000 - $205,000

About The Position

Xaira Therapeutics 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. We are looking for a Principal Scientist with a strong background in immunology to identify new therapeutic opportunities and help shape Xaira’s portfolio. You will integrate internal datasets, human genetics, and published research to prioritize opportunities that address patient needs and build on Xaira’s target discovery and antibody design platforms. This role is ideal for someone who enjoys connecting scientific findings and translating them into strategic decisions.

Requirements

PhD in immunology or a related field with 6+ years of industry experience in drug discovery, target discovery, preclinical drug development, or translational drug development.
Experience drawing on human genetics, genome-scale multi-omics datasets, and other human data alongside cell biology, disease biology, and clinical findings to evaluate therapeutic hypotheses.
Strong scientific judgment and the ability to distinguish an interesting association from a credible therapeutic opportunity.
Clear communication skills and the ability to guide decisions across disciplines.
Curiosity, independence, and a willingness to challenge assumptions and revise recommendations as the evidence evolves.

Responsibilities

Identify promising disease areas. Assess unmet needs, disease mechanisms, patient subsets, and the limitations of current treatments.
Find and prioritize targets. Combine human genetics, functional genomics, genome-scale multi-omics datasets, and patient data to develop strong therapeutic hypotheses.
Assess platform fit. Work with molecular design, protein engineering, and functional screening teams to identify opportunities where our platforms could offer a therapeutic advantage.
Evaluate the evidence. Assess causality, the desired direction of target modulation, safety risks, and key knowledge gaps.
Build clear recommendations. Develop concise target assessments covering disease rationale, patient selection, competition, differentiation, and risks.
Track emerging science and competition. Follow publications and clinical results, and monitor the competitive landscape to identify new opportunities and inform priorities.
Use AI tools to analyze multi-modal data.
Lead discovery programs or platform initiatives.