Founded by 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 society’s toughest challenges – from eradicating disease, to improving education. Across our core initiatives of Science and Education, we’re pairing engineering with grantmaking, impact investing, policy work, and advocacy, to progress in our mission of building an inclusive, just and healthy future for everyone.
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.
Meta's Knowledge Graph Engineering Team (KGE) builds and supports meta.org, enabling researchers to access our comprehensive knowledge graph of scientific research, with features and tools, to help them discover new research and stay up to date.
Members of the team have a direct impact on all features of Meta and its products to accelerate science and literature discovery. A person in this role not only works closely with their team members but with the product, research science and analytics teams to design, build and support technical solutions. This individual will also support us in our ongoing goal of cultivating a culture of shared best practices and knowledge around data engineering.
- Design, build, analyze and improve the efficiency, stability, and resiliency of data processing pipelines and Meta's knowledge graph for scientific literature data
- Design and implement robust machine learning pipelines
- Collaborate with the shared infrastructure team and evangelize best practices in the team
- 8+ years relevant coding experience
- Amazon Web Services (AWS) or similar cloud providers
- Experience with an object-oriented systems language such as Java
- Experience with modern and leading big data processing frameworks like Apache Spark
- Experience with Kubernetes, Docker, Terraform, Travis CI, Jenkins, and other infrastructure/CI tools
- Experience with DevOps best practices
- Experience with a scripting language such as Bash & python
- Familiarity with Machine Learning & Text Mining (NLP) libraries & packages such as scikit-learn, Pandas, Polyglot, NLTK, Stanford CoreNLP a plus