Staff Software Engineer, Science

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.

The Team

CZI supports the science and technology that will make it possible to help scientists cure, prevent, or manage all diseases by the end of this century. While this may seem like an audacious goal, in the last 100 years, biomedical science has made tremendous strides in understanding biological systems, advancing human health, and treating disease.

Achieving our mission will only be possible if scientists are able to better understand human biology. To that end, we have identified four grand challenges that will unlock the mysteries of the cell and how cells interact within systems — paving the way for new discoveries that will change medicine in the decades that follow:

Building an AI-based virtual cell model to predict and understand cellular behavior
Developing state-of-the-art imaging systems to observe living cells in action
Instrumenting tissues to better understand inflammation, a key driver of many diseases
Engineering and harnessing the immune system for early detection, prevention, and treatment of disease

CZI’s work in science includes grantmaking programs, open-source software development, and close collaboration with the Chan Zuckerberg Biohub Network. The CZ Biohub Network includes the San Francisco, Chicago, and New York Biohubs as well as the Chan Zuckerberg Imaging Institute. CZI also collaborates with institutional partners like the Kempner Institute for the Study of Natural & Artificial Intelligence at Harvard University. Join us in accelerating science.

The Opportunity

The Data Engineering team manages and processes scientific datasets specifically designed to enable biological modeling. It is responsible for data validation, wrangling, testing, storage, and retrieval. We handle over 8+ million unique cells worth of single cell transcriptomic data, over 15 thousand cryoET tomograms that are in imaging datasets as large as 20TB and counting, and will be expanding to support larger scale and additional imaging, sequencing, and literature modalities. Our resources provide access to open source data that is structured and used by tens of thousands of scientists each month to quickly query and form hypotheses on understanding how genetic variants in cells impact disease risk, define drug toxicities, and eventually discover better therapies.

As a staff software engineer on the Data Engineering team, you will design and implement all the data needs for our platforms, CELLxGENE Discover, CryoET, as well as the new platform we are building that has a focus on data for AI and the virtual cell, in order to enable scientists to further interrogate our very large and growing corpus of data without any need to download the data itself or have any computational expertise. You will work on a collaborative, multidisciplinary team to develop solutions for our scientist users to accelerate their workflows and accelerate the pace of scientific discovery. You will be responsible for setting the direction of how our teams ingest, transform, validate, process, store, monitor, and utilize petabytes of data for ease of use, search and modeling. You will also be responsible for upscaling the engineers around you and influencing the proper technical best practices and data design for efficient and effective delivery.

No prior biology experience is required for this role. You will have the opportunity to pair with Computational Biologists to develop solutions for our users and be able to learn about biology from experts on our team.

Our tech stack: Python, Terraform, AWS infrastructure, TileDB.

What You'll Do

Develop end-to-end, robust data pipeline architectures that seamlessly integrate data ingestion, preprocessing, feature engineering, model training, and deployment.
Implement scalable data warehousing solutions to handle massive volumes of single-cell transcriptomics data and imaging data.
Ensure data security and compliance with industry standards and regulations.
Implement optimization strategies such as data partitioning, indexing, and compression to enhance query performance and reduce computational costs.
Create user-friendly APIs to enable researchers and scientists to easily access and explore the curated data.
Develop scalable, maintainable, and testable software systems and participate in team conversations and efforts on engineering excellence.
Collaborate with product managers, computational biologists, UX designers, and other software engineers to deliver constant incremental value for scientists without compromising on software quality.
Have opportunities to learn about scientific data and technologies, though no prior experience is required!