Founded by Dr. Priscilla Chan and Mark Zuckerberg in 2015, the Chan Zuckerberg Initiative (CZI) is a new kind of philanthropy that’s leveraging technology to help solve some of the world’s toughest challenges – from eradicating disease, to improving education, to reforming the criminal justice system. Across three core Initiative focus areas of Science, Education and Justice and Opportunity,  we’re pairing engineering with grantmaking, impact investing, policy work, and movement building, to help build an inclusive, just and healthy future for everyone.

Our Values

We believe we can help build a future for everyone.

  • We aim to be daring, but humble:  We look for bold ideas — regardless of structure and stage — and help them scale by pairing engineers with subject matter experts to build tools that accelerate the pace of social progress.
  • We want to learn fast, but build for the long-term: We want to iterate fast and help bring new solutions to the table, but we also realize that important breakthroughs often take decades, or even centuries.
  • Stay close to the real problems: We engage directly in the communities we serve because no one understands our society’s challenges like those who live them every day.  

Our success is dependent on building teams that include people from different backgrounds and experiences who can challenge each other's assumptions with fresh perspectives. To that end, we look for a diverse pool of applicants including those from historically marginalized groups — women, people with disabilities, people of color, formerly incarcerated people, people who are lesbian, gay, bisexual, transgender, and/or gender nonconforming, first and second generation immigrants, veterans, and people from different socioeconomic backgrounds.

The Opportunity

By pairing engineers with leaders in our education, science, and justice and opportunity teams, we can bring 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 and supporting scientists around the world as they develop a comprehensive reference atlas of all cells in the human body.

The Head of Artificial Intelligence will build, lead, and manage the machine learning research team for Scientific Knowledge, which operates at the intersection of CZI Technology (direct reporting) and CZI Science. The primary focus is to help accelerate scientific discoveries in biomedical research by making and applying breakthroughs in biomedical natural language processing, knowledge representation and reasoning.

We believe that the complexity and scale of scientific knowledge is limiting how quickly researchers can understand, navigate and quickly make powerful connections across what is known to solve problems and generate hypotheses. This is one of the fundamental challenges hindering the rate of discovery in basic scientific research.

The Head of Machine Learning will play a pivotal and impactful role in solving this challenge for 5 million biomedical researchers. By building and leading the machine learning research team and collaborating with leading academic machine learning researchers, the person in this role will help enable scientists to navigate a structured form of knowledge contained within unstructured text. In building systems that can read natural language text to extract biomedical facts, identify classes of biomedical entities, and understand the relationships between them — using deep neural networks, supervised or semi-supervised learning and other advanced techniques — CZI will provide tools to help all researchers worldwide, for free.

This person needs to fluidly balance research at the leading edge of natural language processing, knowledge representation/reasoning and related ML fields, with the ability to pragmatically apply new and existing techniques and models into a production knowledge graph at scale. 

You will

  • Build, lead and manage the machine learning research team that is advancing CZI's efforts in scientific knowledge understanding, specifically for the Meta product
  • Own the success of all ML models (e.g. disambiguation, predictions, recommendations, document structure, metadata extraction, entity mention extraction and resolution, entity-type assignment, relation extraction, chains of reasoning)
  • Establish the research agenda, priorities and staffing (internal and external partnerships)
  • Direct and oversee long term research projects, along with short-term production-driven model iterations
  • Influence progress of relevant computer science research communities by producing publications, software, and data sets that can be reused
  • Drive outcomes in collaboration with research partners in academia, other philanthropies and mission-driven organizations
  • Collaborate with engineering and product teams to incorporate the output of the ML team into Meta’s knowledge graph and tools
  • Partner with CZI leadership and peers across the CZI Science and CZI Technology teams
  • Provide an an expert perspective on ML/AI related strategy, tactics and issues across CZI and with partner organizations
  • Set and maintain a high quality bar in hiring, models, code, and process

You have

  • PhD and/or recognized expert in Computer Science, Artificial Intelligence, Machine Learning, or related technical fields (computational statistics, applied mathematics, natural language processing, deep learning, knowledge graphs, reinforcement learning, data representation)
  • Track record of leading machine learning teams to produce models for use in scalable, and preferably biomedical, applications and products
  • Publications in ML/AI, computer science, statistics, applied mathematics, data science, or related technical fields
  • Comprehensive knowledge of modern and leading edge ML tools and techniques
  • Strong ability to communicate machine learning methods and outcomes to AI experts, scientists, and technologists



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