Machine Learning Scientist, Foundation Virtual Staining Models; Computational Microscopy - Bioh

Machine Learning Scientist, Foundation Virtual Staining Models (Computational Microscopy - Biohub SF) Job Description

The Chan Zuckerberg Biohub San Francisco (CZ Biohub SF) is an independent nonprofit research institute that brings together three powerhouse universities - Stanford, UC Berkeley, and UC San Francisco - into a single collaborative technology and discovery engine. CZ Biohub SF itself supports some of the brightest, boldest engineers, data scientists, and biomedical researchers to investigate the fundamental mechanisms underlying disease and develop new technologies that will lead to actionable diagnostics and effective therapies.

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.

• 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 San Francisco 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.

Reporting to the Platform Leader of Computational Microscopy, the Machine Learning Scientist will have the primary responsibility for developing innovative and impactful AI/ML models of intracellular dynamics and accelerating CZ Biohub’s ambitious goals of mapping dynamic cell systems across biological scales. Please include a cover letter describing the match between your career trajectory and this role (required). If you have letters of recommendation that you would like to share before the interviews, please mention them in the cover letter.

This role will be based in San Francisco initially, with a planned transition to Redwood City in two years.

• Design imaging experiments in collaboration with experimental colleagues. You will need to be able to join the colleagues in BSL2 (Biosafety level
  2) laboratory to discuss experiments and data.
• Develop explainability methods that provide actionable insights in model performance and testable biological hypotheses for interactions among organelles and cells in the presence of intrinsic and extrinsic perturbations.
• Contribute to and extend our computational pipelines for training models with terabyte‑scale imaging data acquired with high‑throughput live‑cell screens.
• Train, deploy, and maintain state‑of‑the‑art deep learning models for use by biologists within the CZ Biohub, partner universities, and beyond.
• Demonstrate scholarly excellence with regular dissemination of the findings via papers, presentations, and computational demonstrations.

• MS degree with 3 years of relevant experience or a recent PhD degree in computer science, bioimaging, clinical imaging, computer vision, bioengineering, or a related field with AI/ML research experience.
• 3+ years of experience with Python and relevant deep learning frameworks (PyTorch, JAX, etc.).
• Strong mathematical foundation in linear algebra, calculus, and optimization algorithms.
• Experience analyzing biomedical datasets to answer biological research questions.
• Track record of preprints or publications focused on computer vision for imaging data.
• Experience working in Linux environments and familiarity with version control systems (e.g., git).
• Experience working with terabyte‑scale datasets and training large AI/ML models.
• An appetite to study and build on state‑of‑the‑art research in AI/ML for cell biology.
• Strong interpersonal skills with excellent written and verbal communication skills.
• Ability to respond quickly to data and needs of colleagues, and prioritize work efficiently.

• Ability and interest in contributing to community standards and open‑source ecosystem.
• Demonstrated software engineering skills in developing robust and…