Computational Biologist II, CellxState

Biohub is the first large-scale initiative bringing frontier AI models, massive compute, and frontier experimental capabilities under one roof. We're building a general‑purpose system to accelerate scientific discovery, integrating frontier AI models, biological foundation models, and lab capabilities, with the ultimate goal of curing disease. Our technology powers scientists around the world, translating AI capabilities into tools that accelerate research everywhere.

Through our multi‑dimensional imaging program, we build imaging tools that capture life across scales — from single proteins to whole organisms — revealing how proteins and cells function, communicate, and assemble into living systems. These observations are laying the groundwork for a new generation of AI models that can predict cellular behavior and guide the development of better treatments for widespread diseases.

Our work brings together three powerhouse universities — Stanford, UC Berkeley, and UC San Francisco — into a single collaborative technology and discovery engine.

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.

Part of the Imaging Grand Challenge, The CELLxSTATE Program builds next‑generation technologies to decode and control how cells make decisions — combining live‑cell imaging, multi‑omics, and AI at unprecedented scale. We also create large reference datasets that can be mined and reused by the entire community for discovery, like our Open Cell project that maps protein localization and interactions ((Use the "Apply for this Job" box below).). Our science is fully open‑source and published in journals such as Science, Nature Methods, and Cell ().

At the core of our current efforts is multiDPS (Multimodal Dynamic Pooled Screening), a high‑throughput platform that integrates custom microscopy, automation, CRISPR screening and molecular profiling to map and predict dynamic cell states.

We are seeking a Computational Biologist to help lead image analysis for our next‑generation Optical Pooled Screening program. This is a great opportunity for candidates with a strong interest in data science, engineering, and cell biology, supported by experts in a highly collaborative and well‑funded scientific environment.

• Design, develop, and maintain scalable image analysis pipelines for large‑scale fluorescent microscopy datasets, with an emphasis on image quality robustness and computational efficiency.
• Integrate emerging multi‑modal data types (e.g., spatial transcriptomics) into unified, AI‑ready datasets that support downstream modeling and discovery.
• Advance our bio‑image analysis capabilities (e.g., segmentation, tracking, image‑stitching, and image registration).
• Partner closely with biologists and automation engineers to implement end‑to‑end quality control metrics, ensuring the fidelity of our experimental and computational pipelines.
• Architect modular and reusable processing frameworks that can flexibly support multiple experiment types within a shared infrastructure.
• Publish and disseminate impactful findings through preprints, papers, and software repositories (e.g., Git Hub).

• PhD in Computational Biology, Biology, or Computer Science, or a MS with relevant job experience.
• At least 4 years of experience in Python‑based image analysis or scientific computing. Experience with fluorescence microscopy, confocal, or light sheet is a plus.
• Fluency with computational tools and infrastructure such as Python, Git Hub, and Slurm.
• Experience with modern biological data formats such as OME‑Zarr and Ann Data.
• Experience designing workflows for large, complex datasets, including scalable storage formats, and reliable metadata and experiment tracking.
• A proven track record…