Applied Research Scientist, Core Machine Learning
(Menlo Park, CA – New York, NY)
Facebook’s mission is to give people the power to share, and make the world more open and connected. Through our growing family of apps and services, we’re building a different kind of company that helps billions of people around the world connect and share what matters most to them. 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 make the world more open and accessible. Connecting the world takes every one of us—and 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
- Broad and deep knowledge of machine learning and deep learning research
- Experience building systems based on machine learning and/or deep learning methods
- Expert knowledge developing and debugging in C/C++, Java, and/or Scala
- Experience with filesystems, server architectures, and distributed systems
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