The Chan Zuckerberg Initiative was founded by Priscilla Chan and Mark Zuckerberg in 2015 to help solve some of society’s toughest challenges — from eradicating disease and improving education to addressing the needs of our local communities. Our mission is to build a more inclusive, just, and healthy future for everyone.

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.

• CZI provides a generous employer match on employee 401(k) contributions to support planning for the future.
• Annual benefit for employees that can be used most meaningfully for them and their families, such as housing, student loan repayment, childcare, commuter costs, or other life needs.
• CZI Life of Service Gifts are awarded to employees to “live the mission” and support the causes closest to them.
• 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 to the Bay Area
• And more!

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.
Explore our work modes, benefits, and interview process at www.chanzuckerberg.com/careers.
#LI-Hybrid

• Develop and apply state-of-the-art methods in artificial intelligence and machine learning to solve important problems in biological imaging such as representation learning, multi-object tracking, multimodal data integration or time series modeling.
• Work with a diverse range of biological data types, including multi-omics data, cell/tissue images, time lapse microscopy, cryo-ET tomography and more.
• Implement scalable solutions with respect to complex problems/scientific questions on large-scale data sets, especially using machine learning approaches, predictive models, and statistical analysis, to advance understanding of cell structures, systems, and interactions.
• Work as part of a team to leverage large open-source biological datasets to create models that transform our understanding of cells.
• Advance the community standards for dissemination, presentation, and evaluation of computational approaches to core scientific problems.
• Contribute to the scientific community through publishing papers, blog posts, open source code, and attending conferences in machine learning and the life sciences.