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We are looking for researchers and applied scientists to join the Central Applied Science team. Central Applied Science is an interdisciplinary team of quantitative scientists that aims to deliver research and innovation that fundamentally contribute to Meta's success. By applying your expertise in quantitative methods, you will be empowered to drive impact across a range of products, infrastructure and company operations.
Job Responsibility:
Work with vast amounts of data, generate research questions that push the state-of-the-art, and build data based products
Develop novel quantitative methods on top of Meta's unparalleled data infrastructure
Learn new tools, systems, and programming languages quickly as required by the particular project you are working on
Communicate best practices in quantitative analysis to partners
Work collaboratively with other scientists, engineers, UX researchers, and product managers to accomplish complex tasks that deliver demonstrable value
Proactively identify, scope and implement innovative solutions with well defined intermediate milestones
Actively identify new opportunities within Meta's long term roadmap for applied science contributions
Requirements:
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
Currently holds a PhD in the field of Operations Research, Computer Science, or a related field
At least 3 years of industry experience as an applied research scientist or a similar role
Experience with empirical research and for answering questions with data
Experience developing algorithms in languages like Python, C, C++ or Java
Experience analyzing datasets using languages like Python
Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment
Nice to have:
Experience with large scale distributed AI training and inference
Experience working and communicating cross functionally in a team environment
Experience analyzing large datasets using tools like Presto, Hive or Spark
Proven track record of publications at leading journals or conferences such as ICML, NeurIPS, ICLR, IJCAI, AAAI, KDD, WWW, JMLR, JACM, MLSys or similar
Experience with solving large-scale combinatorial optimization problems