Computational Biologist, Immune Perturbation
About The Position
Biohub is a 501(c)(3) biomedical research organization building the first large-scale scientific initiative combining frontier AI with frontier biology to solve disease. We build the technology to help scientists around the world use AI-powered biology to study how cells operate, organize, and work as part of systems to understand why disease happens and how to correct it. With our compute capacity, AI research and engineering, and state-of-the-art technology for measuring, imaging, and programming biology, we are enabling scientists worldwide to use AI-powered biology to advance our understanding of human health. The Team Our decoding inflammation team builds tools to enable precise molecular-level measurements of inflammation within human tissues in real time, and develop proactive, early interventions that can be deployed when inflammation — which underlies the most significant causes of death worldwide — first flares in the body. You can learn more about our work here. Our team collaborates with three powerhouse universities - Northwestern University, the University of Chicago, and the University of Illinois Urbana-Champaign - to develop first-in-class technologies and make breakthroughs. Our Vision Pursue large scientific challenges that cannot be pursued in conventional environments Enable individual investigators to pursue their riskiest and most innovative ideas Facilitate research by scientists and clinicians at our home institutions and beyond We are a team of passionate individuals powered by technology, guided by scientific research, and driven by collaboration, working toward a mission to cure or prevent all disease. The Opportunity Biohub is seeking a Computational Biologist to join our interdisciplinary AI/ML team within the Virtual Immune System initiative. This is a hands-on research role focused on building and evaluating lab-in-the-loop experimental systems and closing the cycle between computational models of immune cell behavior and wet-lab validation. The ideal candidate brings strong biological intuition, computational rigor, and experience applying machine learning to genomics or perturbation biology. You will work at the intersection of foundation models, reasoning systems, and experimental immunology — developing frameworks for how these tools can be integrated into experimental workflows. This means defining what questions are addressable, designing experiments that stress-test model predictions, guiding analysis, and evaluating performance across the loop. The environment is highly collaborative and interdisciplinary, spanning immunology, automation engineering, and machine learning, with direct applications to human health and disease.
Requirements
- PhD in Immunology, Computer Science/Statistics, Computational Biology/Genomics, or a related field.
- 3 to 5 years of experience of applying machine learning and computational approaches to biological problems, particularly in genomics or genetic perturbation screening (e.g., CRISPR-based screens, Perturb-seq).
- Hands-on experience with foundation models, LLMs, or reasoning models and a drive to rigorously evaluate where they do and don't work when applied to biological questions.
- Strong biological intuition and a clear scientific point of view — able to identify which questions are tractable, design experiments that test model predictions, and know when results don't pass the smell test.
- Strong programming and data skills (Python preferred), with an emphasis on reproducibility and clear documentation.
- Effective cross-domain communicator with experience working alongside experimentalists, engineers, or other non-computational collaborators to translate between domains and validate computational predictions.
Responsibilities
- Design computationally-guided experiments leveraging reasoning models, foundation models, and automated lab infrastructure to address open questions in immunology and inflammation.
- Partner with experimental scientists, automation engineers, and AI/ML researchers to define addressable biological questions, close the loop between model predictions and wet-lab validation, and iterate on experimental strategy.
- Develop and document best practices for designing experimental workflows, including data acquisition strategy, analysis pipelines, and frameworks for iterative refinement across the loop.
- Critically evaluate model predictions by stress-testing outputs against external datasets, known biology, and expert intuition — identifying failure modes and feeding improvements back into model development.
- Contribute to publications, open-source tools, and translational applications that advance the field of computationally driven immune system modeling.
Benefits
- Provides a generous employer match on employee 401(k) contributions to support planning for the future.
- Paid time off to volunteer at an organization of your choice.
- Funding for select family-forming benefits.
- Relocation support for employees who need assistance moving
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
Ph.D. or professional degree
