Software Engineer, Content Search Relevance

(Seattle, WA)

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.

Facebook is seeking Machine Learning Engineers to join our Content Search & Relevance engineering team. This team has two primarily responsibilities: working on semantic similarity and the relevance ranking framework. The team learns the topical similarities between the query and the document using 1/ neural embeddings of the query and document optimized to detect relevance and 2/ sparse models. The team also builds the framework that provides easy and clean ways to use the new query and document understanding techniques in query rewriting and document scoring. We have rich annotations on freeform text queries and documents, retrieval and ranking stages and the need to consider the query-to-document relevance problem.The ideal candidate will have industry experience working on a range of classification problems, ranking, regression, clustering, natural language processing (query understanding, document understanding), information retrieval, supervised machine learning, and deep learning.

Responsibilities

  • Develop highly scalable classifiers and tools leveraging machine learning, data regression, and rules based models
  • Suggest, collect and synthesize requirements and create effective feature roadmap
  • Code deliverables in tandem with the engineering team
  • Adapt standard machine learning methods to best exploit modern parallel environments (e.g. distributed clusters, multicore SMP, and GPU)

Minimum Qualifications

  • MS degree in Computer Science or related quantitative field or Ph.D degree in Computer Science or related quantitative field
  • 5+ years of experience in one or more of the following areas: machine learning, recommendation systems, pattern recognition, data mining or artificial intelligence
  • Proven ability to translate insights into business recommendations
  • Experience with Hadoop/Hbase/Pig or MapReduce/Sawzall/Bigtable
  • Knowledge developing and debugging in C/C++ and Java
  • Experience with scripting languages such as Perl, Python, PHP, and shell scripts

Preferred Qualifications

  • Experience with filesystems, server architectures, and distributed systems

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