Chan Zuckerberg Biohub Network

About the Company

The Chan Zuckerberg Biohub Network is a group of nonprofit research institutes that bring together scientists, engineers, and physicians with the goal of pursuing grand scientific challenges on 10- to 15-year time horizons. The CZ Biohub Network focuses on understanding underlying mechanisms of disease and developing new technologies that will lead to actionable diagnostics and effective therapies. CZ Biohub San Francisco -- which was the inaugural Biohub and launched in 2016 -- works on elucidating dynamic cell systems across scales in health and disease, joining forces with the Bay Area's leading academic institutions -- Stanford University, UC Berkeley, and UC San Francisco -- to do bold, visionary science that can't be done elsewhere. CZ Biohub Chicago, which launched in 2023, focuses on engineering technologies to make precise, molecular-level measurements of biological processes within human tissues, with an ultimate goal of understanding and treating the inflammatory states that underlie many diseases. It catalyzes collaboration between the University of Chicago, Northwestern University, and the University of Illinois Urbana-Champaign. CZ Biohub New York, which launched in 2023, brings together Columbia University, The Rockefeller University, and Yale University to bioengineer immune cells to sense and record signals of disease and adapt these cells to spot diseases such as lethal cancers and Alzheimer's in their earliest stages, long before they are usually diagnosed.

Listed Jobs

Company Name

Chan Zuckerberg Biohub Network

Job Title

AI Research Intern (PhD), AI Research, Science

Job Description

**Job Title** AI Research Intern (PhD) – AI Research, Science **Role Summary** A 12‑week (or longer) research internship focused on developing frontier AI systems for biology. Interns train deep learning models, build biological foundation models and reasoning agents, and contribute to publications and open‑source releases while collaborating with cross‑disciplinary scientists. **Expectations** - Enrolled in a PhD program in Machine Learning/AI, computational biology, physics, applied mathematics, or a closely related quantitative field. - Produce research outcomes that include code, model training pipelines, evaluations, and publishable results. - Work independently while engaging with a team of researchers, engineers, and wet‑lab scientists. - Communicate findings clearly across disciplines. - Maintain production‑quality code and document work for open‑source release. - Have US work authorization (no sponsorship provided). **Key Responsibilities** - Conduct cutting‑edge research on training deep learning models for biological data. - Design, implement, and iterate on data pipelines, model training, and evaluation workflows. - Develop and fine‑tune large language models for biological reasoning and understanding. - Build and test agentic systems capable of autonomous biological investigations. - Collaborate with wet‑lab scientists to translate research ideas into experimental designs. - Contribute to team publications, conference papers, and open‑source releases. - Mentor junior members and present progress to the broader AI research team. **Required Skills** - Active PhD enrollment in ML/AI or related quantitative discipline. - Demonstrated research record (publications, fellowships, grants, patents). - Proficiency in deep learning frameworks, primarily PyTorch. - Ability to design scalable training pipelines and evaluate model performance. - Strong written and verbal communication across technical and non‑technical audiences. - Self‑motivation, initiative, and ability to work autonomously. - Familiarity with biological data modalities (sequence, structure, imaging, omics) is a plus but not required. **Required Education & Certifications** - PhD candidacy in Machine Learning/AI, Computational Biology, Physics, Applied Mathematics, or a closely related quantitative field. - No specific certifications required.

New york city, United states

Hybrid

Fresher

27-02-2026