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.

The Team

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. You can learn more about our work here.


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.
The Opportunity

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 OpenCell project that maps protein localization and interactions ( Our science is fully open-source and published in journals like 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. We embrace team science and our projects bring together biologists, technology developers, engineers, data scientists, and AI/ML experts.
What You'll Do

• 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., GitHub).

What You'll Bring

• 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 lightsheet is a plus.
• Fluency with computational tools and infrastructure such as Python, Github, and Slurm.
• Experience with modern biological data formats such as OME-Zarr and AnnData.
• Experience designing workflows for large, complex datasets, including scalable storage formats, and reliable metadata and experiment tracking.
• A proven track record of individual innovation, together with a strong ability to work collaboratively.
• A passion for research and understanding how cells work.
• An enthusiasm for team science and open science - this position will be embedded in a large multi-disciplinary team.
• Excellent written and oral communication skills.

Compensation

The San Francisco, CA base pay range for a new hire in this role is $153,000 - $210,100. New hires are typically hired into the lower portion of the range, enabling employee growth in the range over time. Actual placement in range is based on job-related skills and experience, as evaluated throughout the interview process.


This position may be eligible to participate in Biohub's discretionary annual performance bonus program. Bonus eligibility and targets are determined in accordance with Biohub's total rewards philosophy and may vary by role.
Better Together

As we grow, we're excited to strengthen in-person connections and cultivate a collaborative, team-oriented environment. This role is a hybrid position requiring you to be onsite for at least 60% of the working month, approximately 3 days a week, with specific in-office days determined by the team's manager. The exact schedule will be at the hiring manager's discretion and communicated during the interview process.
Benefits for the Whole You

We're thankful to have an incredible team behind our work. To honor their commitment, we offer a wide range of benefits to support the people who make all we do possible.

• 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

If you're interested in a role but your previous experience doesn't perfectly align with each qualification in the job description, we still encourage you to apply as you may be the perfect fit for this or another role.

#LI-Hybrid