Senior Director of AI Infrastructure & Engineering, Central Technology

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.

Our Central Tech team provides technology and security support for CZI and our grantees. We believe that Engineering, IT and Security are most effective when in sync and learning from each other on a daily basis.  Across our three pillars of Infrastructure, Security, and Grantee & Partner Support, we enable our teams to achieve their goals faster and more securely. We leverage technology to automate manual processes, constantly innovate to optimize operations, provide first-class support, and build solutions to enable the scale and execution of our business partners' strategies and initiatives.

The AI/ML Infrastructure team works on building shared tools and platforms to be used across the Chan Zuckerberg Initiative, partnering and supporting the work of an extensive group of Research Scientists, Data Scientists, AI Research Scientists, as well as a broad range of Engineers focusing on Education and Science domain problems. Members of the shared infrastructure engineering team have an impact on all of CZI's initiatives by enabling the technology solutions used by other engineering teams at CZI to scale.

The Opportunity

As a Research Engineer on the AI Engineering 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 work as part of a team responsible for developing and deploying AI models that use data developed by CZI and research partners all for the purpose of contributing to greater understanding of human cell function.

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 single cell biology, imaging, genomics, and proteomics.

What You'll Do

• Working with the AI Research Scientists, iterate on, optimize, deploy, and maintain innovative machine learning models, systems, and software tools that enable the analysis and interpretation of AI models for Biology
• Work with cross-functional team members to quickly iterate on system performance to meet/stay ahead of users’ needs - e.g. we get feedback that the model doesn't scale to X million so working with our user researcher/scientist/product team to iterate on the solution. 
• Partner with research scientists to build robust data loader pipelines for scalable distributed training and evaluation.
• Serve as an interface to product and engineering teams to understand how models may need to evolve to support multiple use cases.
• Develop model evaluation and interpretability frameworks that help biologists understand which data features drive model predictions
• Build reusable engineering utilities that can unlock experimentation velocity across research initiatives in the organization
• Optimize model architectures to enhance performance, fine-tune accuracy, and efficiently manage infrastructure resources

What You'll Bring

• Experience in working with a highly interactive and cross-functional collaborative environment with a diverse team of colleagues and partners solving complex problems through applied deep learning.
• A track record and expertise in developing deep learning models on large-scale GPU clusters, using techniques of distributing training such as DDP, FSDP, Model parallelism, low-precision training, profiling and optimizing AI/ML code, fine tuning models.
• Expertise in leading end-to-end experimentation pipelines for training and evaluating deep learning models, with particular focus on experiment tracking and reproducibility.
• A good working knowledge of Python-based ML libraries and frameworks such as PyTorch, JAX, TensorFlow, NumPy, Pandas, and Scikit-learn.
• Experience in using modern frameworks for distributed computing and infrastructure management, particularly as related to ML models such as PyTorch Lightning, Deepspeed, TransformerEngine, RayScale etc.
• Ability to effectively balance exploratory research with robust engineering practices.
• A good working knowledge of general software engineering practices in a production environment.
• The ability to work independently and as part of a team, and have excellent communication and interpersonal skills.
• Have a Masters in computer science with a focus on machine learning & data analytics, or equivalent industry experience and at least 6-8 years of experience developing and applying machine learning methods.

Compensation

The Redwood City, CA base pay range for a new hire in this role is $241,000 - $331,000. 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. 

Work Mode

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. 

• 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.

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