Research Scientist, AI/ML (Biohub Chicago - VIS)

The Chan Zuckerberg Biohub Chicago (https://www.czbiohub.org/chi) is an independent nonprofit research institute that brings together three powerhouse universities - Northwestern University, the University of Chicago, and the University of Illinois Urbana-Champaign - into a single collaborative technology and discovery engine. CZ Biohub itself supports some of the brightest, boldest engineers, data scientists, and biomedical researchers to make breakthroughs in medicine and develop new technologies, frequently in collaboration with our partner universities. We are guided by our values of scholarly excellence; disruptive innovation; hands-on engineering/hacking/building; partnership and collaboration; open communication and respect; inclusiveness; and opportunity for all.

Our Vision

• We pursue large scientific challenges that cannot be pursued in conventional environments
• We enable individual investigators to pursue their riskiest and most innovative ideas
• The technologies developed at CZ Biohub Chicago facilitate research by scientists and clinicians at our home institutions and beyond

Diversity of thought, ideas, and perspectives are at the heart of CZ Biohub Network and enable disruptive innovation and scholarly excellence. We are committed to cultivating an organization where all colleagues feel inspired and know their work makes an important contribution.

The Opportunity

The Chan Zuckerberg Biohub Chicago is seeking an accomplished Research Scientist to join our interdisciplinary AI/ML team. This role requires experience in research settings, and a proven ability to design, evaluate, and publish innovative computational methodologies that leverage machine learning, statistics, and AI to advance biological research and discovery. This is a hands-on role in a dynamic and interdisciplinary environment, with opportunities to work at the forefront of technology development and application in genomics, immunology, and AI-driven discovery. This role will be based at the CZ Biohub Chicago.  The candidate should have a strong foundational understanding of biology and biochemistry with an established track record of applying AI tools to biological problems in a highly interdisciplinary environment.

You’ll work in a hands-on, highly collaborative, and interdisciplinary environment spanning immunology, structural biology, and machine learning.  This is a unique opportunity to help shape next-generation computational immunology tools with direct applications in human health and disease.

What You'll Do

• Develop and benchmark predictive models of immune interactions using AI/ML, protein language models, and structure prediction tools.
• Validate, interpret, and stress-test model predictions using external datasets and expert biological intuition.
• Collaborate with lab scientists and computational researchers to ensure real-world relevance and robust model evaluation.
• Create intuitive visualizations and interfaces to interpret molecular interactions and support downstream experimentation.
• Contribute to impactful publications, open-source software, and potential translational applications through patents or partnerships.

You have 

Essential -

• PhD in Immunology, Structural Biology, Biochemistry, Computational Biology, or a related field.
• Demonstrated experience applying machine learning to biological problems, particularly involving proteins or immune recognition.
• Proficiency with protein language models (e.g., ESM2, EVO), structure prediction tools (e.g., AlphaFold2), and model interpretation techniques.
• Working knowledge of model development pipelines, including feature selection, regularization, and introspection.
• Familiarity with protein–protein interaction networks and the biophysical principles of molecular recognition.
• Strong coding and data skills, with an emphasis on reproducibility, clarity, and documentation.
• Excellent collaborative and communication skills, with experience in interdisciplinary teams.

Nice to have - 

• Experience collaborating closely with experimentalists to validate computational predictions.
• Prior work in immunoinformatics, AI-guided protein design, or immune repertoire anal