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 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. 

We build open source software tools and leverage artificial intelligence to accelerate the pace of biomedicine. We fund scientific research worldwide to advance the frontiers of knowledge. And we launched a family of institutes to do research that can’t be done in conventional environments. Each aspect is essential to our approach to building for the long term.

CZI’s work in science includes grantmaking programs, open-source software development, and close collaboration with its partner institutes at 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 AI/ML team is funding and building one of the largest computing systems dedicated to nonprofit life science research in the world. This new effort will provide the scientific community with access to predictive models of healthy and diseased cells, which will lead to groundbreaking new discoveries that could help researchers cure, prevent, or manage all diseases by the end of this century.

As an Engineer on the AI/ML team you will apply and optimize state-of-the-art models in artificial intelligence and machine learning to solve important problems in the biomedical sciences aligned with CZI’s mission. You will be expected to be familiar and willing to push the boundaries of generative AI tools for scientific discovery. You will work as part of a team responsible for developing and deploying (Gen-) AI models that use data developed by CZI and research partners all for the purpose of contributing to greater understanding of human cell function and AI for science. You will interface with our AI cluster infrastructure team to build and maintain world class AI modeling capabilities. You will have the opportunity to work closely with teams of scientists, computational biologists, engineers within CZI and to collaborate with CZI grantees, with CZ institutes, and other external labs and organizations. Your work will inspire and enhance the production and analysis of datasets by CZ teams and collaborators. Scientific focus areas could include literature agents, models for single cell biology, imaging, genomics, and proteomics.

What You'll Do

  • Work with the Research Scientists, iterate on, optimize, deploy, and maintain large-scale machine learning models, systems, and software tools that enable the analysis and interpretation of complex biology data sets and natural language.
  • Work with the team on building, deploying, maintaining AI agents for science
  • Work with the cross-functional team members to quickly iterate on system performance and scaling to meet/stay ahead of users’ needs - 
  • Build, train, and evaluate multimodal foundation models utilizing CZI’s 1000xH100 GPU compute cluster and distributed training infrastructure. 
  • Work with a diverse range of multimodal and biological data types, including multi-omics data, cell/tissue images, human specimen/cohort data, cryoET tomography, scientific literature, and more. 
  • May be involved in data pipelining work to clean, manage, and version data to ensure that the Research Scientist has access to reproducible data. 
  • Serve as an interface to product teams to understand how models may need to evolve to support multiple use cases.
  • Interact with the community through open science practices

What You'll Bring

  • Enjoy working in a highly interactive and cross-functional collaborative environment with a diverse team of colleagues and partners in leading-edge cell biology data-driven research.
  • Proven track record in developing AI/ML models for large-scale clusters (CPUs and GPUs) and distributing computational load across multiple nodes to optimize performance. 
  • Knowledge of deploying models for both batch and real-time (endpoint) inference
  • Experience in fine-tuning and/or pre-training large models with optimized code to maximize hardware utilizationA good working knowledge of Python-based ML libraries and frameworks such as PyTorch, TensorFlow, NumPy, Pandas, and Scikit-learn.
  • Nice to have: familiarity/ability with lower level languages like Rust, C++ (i.e. to modify CUDA kernels if necessary)
  • Ability to expand core pieces of AI software libraries (pytorch, (Num)pyro, etc.) or build new ones if necessary
  • Expertise in using modern frameworks for distributed computing and infrastructure management, particularly as related to ML models, (e.g Ray, High Performance Compute (HPC), Distributed PyTorch, etc)
  • Have a Masters or Ph.D in computer science with a focus on machine learning & data analytics, or equivalent industry experience and at least 3-5 years of experience developing and applying machine learning methods.
  • A good working knowledge of general software engineering practices in a production environment. Including experience with control systems like Git and containerization technologies like Docker and Kubernetes. 
  • The ability to work independently and as part of a team, and have excellent communication and interpersonal skills.

Compensation

The Redwood City, CA base pay range for this role is $214,000 - $321,000. 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. Pay ranges outside Redwood City are adjusted based on cost of labor in each respective geographical market. Your recruiter can share more about the specific pay range for your location during the hiring 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. 

  • 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!

Commitment to Diversity

We believe that the strongest teams and best thinking are defined by the diversity of voices at the table. We are committed to fair treatment and equal access to opportunity for all CZI team members and to maintaining a workplace where everyone feels welcomed, respected, supported, and valued. Learn about our diversity, equity, and inclusion efforts. 

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 modesbenefits, and interview process at www.chanzuckerberg.com/careers.

#LI-Hybrid

         
 

Salary

$214,000 - $321,000

Yearly based

Location

Redwood City, CA (Open to Flex)

Job Overview
Job Posted:
3 months ago
Job Expires:
Job Type
Full Time

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