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Applied Research Scientist, Core Machine Learning
Facebook's mission is to give people the power to build community and bring the world closer together. Through our family of apps and services, we're building a different kind of company that connects billions of people around the world, gives them ways to share what matters most to them, and helps bring people closer together. Whether we're creating new products or helping a small business expand its reach, people at Facebook are builders at heart. Our global teams are constantly iterating, solving problems, and working together to empower people around the world to build community and connect in meaningful ways. Together, we can help people build stronger communities — we're just getting started.
We are looking for experienced applied researchers in machine learning and AI with strong software engineering skills. The Core Machine Learning Team is part of the Applied Machine Learning organization. The team carries out applied research in ML/AI and designs, develops and deploys state of the art ML/AI algorithms to the rest of Facebook. The team has developed and optimized various algorithms including Neural Networks, Boosted Decision Trees, Sparse Linear Models, and Deep Learning for several ranking teams including Ads, Feed, Search, Instagram and others.
• Develop highly scalable algorithms based on state-of-the-art machine learning and neural network methodologies
• Combine broad and deep knowledge of relevant research domains with the ability to synthesize a wide range of requirements to make significant contributions to the feature roadmap for the applied machine learning platform
• Apply expert coding skills to platform development projects in partnership with other engineers on ranking and infrastructure teams
• Adapt machine learning and neural network algorithms and architectures to best exploit modern parallel environments (e.g. distributed clusters, multicore SMP, and GPU)
• MS degree in Computer Science or related quantitative field with 5+ years of relevant experience, or Ph.D degree in Computer Science or related quantitative field
• Knowledge of machine learning and deep learning research
• Experience building systems based on machine learning and/or deep learning methods
• Knowledge developing and debugging in C/C++, Java, and/or Scala
• Experience with filesystems, server architectures, and distributed systems