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We’re looking for applied scientists with substantial industry experience to join the Central Applied Science team. Central Applied Science is home to experts from many scientific fields, partnering across the company to deliver engineering systems that bring research and innovation to fundamentally contribute to Meta's success.
Job Responsibility
Work with vast amounts of data, generate research questions that push the state-of-the-art, and build data-driven products which impact the business
Develop novel quantitative methods on top of Meta's unparalleled data infrastructure
Work towards long-term ambitious research goals, while identifying intermediate milestones
Communicate best practices in quantitative analysis to partners
Work both independently and collaboratively with other scientists, engineers, and product managers to accomplish complex tasks that deliver demonstrable value to Meta's community of over 3.8 billion users
Actively identify new opportunities for scientific tooling and systems to yield outsized impact, in line with Central Applied Science's role and mission within Meta
Requirements
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
PhD in computer science, statistics, operations research or a related field
4+ years of industry experience in an applied R&D capacity or similar function
Publications in Machine Learning, AI, computer science, statistics, data science, or related technical fields
Experience analyzing datasets using languages such as Python
Experience using machine learning and deep learning frameworks, such as PyTorch, TensorFlow or scikit-learn
Experience developing algorithms in languages such as Python, C, C++ or Java
Experience analyzing large datasets using tools such as Presto, Hive or Spark
Must obtain work authorization in country of employment at the time of hire, and maintain ongoing work authorization during employment
Nice to have
Track record of building end-to-end systems which bring science and engineering to solve critical business problems
Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
Engagement with leaders to drive decision-making based on a thorough understanding of science and business constraints