Job Description

The Team

Across our work in Science, Education, and within our communities, we pair technology with grantmaking, impact investing, and collaboration to help accelerate the pace of progress toward our mission. Our Central team provides the support needed to push this work forward.

The Central team at CZI consists of our Finance, People & DEI, Real Estate, Events, Workplace, Facilities, Security, Brand & Communications, Business Systems, Central Operations, Strategic Initiatives, and Ventures teams. These teams provide strategic support and operational excellence across the board at CZI.

The Opportunity

By pairing engineers with leaders in our science and education teams, we can bring AI/ML technology to the table in new ways to help drive solutions. We are uniquely positioned to design, build, and scale software systems to help educators, scientists, and policy experts better address the myriad challenges they face. Our technology team is already helping schools bring personalized learning tools to teachers and schools across the country. We are also supporting scientists around the world as they develop a comprehensive reference atlas of all cells in the human body, and are developing the capacity to apply state-of-the-art methods in artificial intelligence and machine learning to solve important problems in the biomedical sciences.

The AI/ML and Data Engineering Infrastructure organization works on building shared tools and platforms to be used across all of the Chan Zuckerberg Initiative, partnering and supporting the work of a wide range 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. A person in this role will build these technology solutions and help to cultivate a culture of shared best practices and knowledge around core engineering.

What You’ll Do

• Participate in the  technical design and building of efficient, stable, performant, scalable and secure AI/ML and Data infrastructure engineering solutions.
• Active hands-on coding working on our Deep Learning and Machine Learning models
• Design and implement complex systems integrating with our large scale AI/ML GPU compute infrastructure and platform, making working across multiple clouds easier and convenient for our Research Engineers, ML Engineers, and Data Scientists.
• Use your solid experience and skills in building containerized applications and infrastructure using Kubernetes in support of our large scale GPU Research cluster as well as working on our various heterogeneous and distributed AI/ML environments.
• Collaborate with other team members in the design and build of our Cloud based AI/ML platform solutions, which includes Databricks Spark, Weaviate Vector Databases, and supporting our hosted Cloud GPU Compute services running containerized PyTorch on large scale Kubernetes.
• Collaborate with our partners on data management solutions in our heterogeneous collection of complex datasets.
• Help build tooling that makes optimal use of our shared infrastructure in empowering  our AI/ML efforts with world class GPU Compute Cluster and other compute environments such as our AWS based services.

What You’ll Bring

• BS or MS degree in Computer Science or a related technical discipline or equivalent experience
• 5+ years of relevant coding experience
• 3+ years of systems Architecture and Design experience, with a broad range of experience across Data, AI/ML, Core Infrastructure, and Security Engineering
• Scaling containerized applications  on Kubernetes or Mesos, including expertise  with creating custom containers using secure AMIs and continuous deployment systems that integrate with Kubernetes or Mesos. (Kubernetes preferred)
• Proficiency with Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure, and experience with On-Prem and Colocation Service hosting environments
• Proven coding ability with a systems language such as Rust,C/ C++, C#, Go, Java, or Scala
• Shown ability with a scripting language such as Python, PHP, or Ruby
• AI/ML Platform Operations experience in an environment with challenging data and systems platform challenges – including large scale Kafka and Spark deployments (or their coralaries such as Pulsar, Flink, and/or Ray) as well as Workflow scheduling tools such as Apache Airflow, Dagster, or Apache Beam
• MLOps experience working with medium to large scale GPU clusters in Kubernetes (Kubeflow),  HPC environments, or large scale Cloud based ML deployments
• Working knowledge of Nvidia CUDA and AI/ML custom libraries.
• Knowledge of Linux systems optimization and administration
• Understanding of Data Engineering, Data Governance, Data Infrastructure, and AI/ML execution platforms.
• PyTorch, Karas, or Tensorflow  experience a strong nice to have
• HPC with and Slurm experience a strong nice to have

How to Apply

Job Types: Full-Time. Salaries: 100,000 and above.

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