Research Scientist, PhD University Grad (Machine Learning)
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.
Facebook’s mission is to connect the world. At Facebook, we use machine learning across a diverse set of applications to help people discover better content more quickly, and to connect with the things that matter most to them. We strive to find ways to deliver more engaging content in News Feed, rank search results more accurately, and present the most relevant ads possible.
In order to meet the demands of our scale, we approach machine learning challenges from a system engineering standpoint, pushing the boundaries of scalable computing and tying together numerous complex platforms to build models that leverage trillions of actions. Our research and production implementations leverage many of the innovations being generated from Facebook’s research in Distributed Computing, Artificial Intelligence and Databases, and run on the same hardware and network specifications that are being open sourced through the Open Compute project.
As a Software Engineer or Research Scientist at Facebook, you will help build the next generation of machine learning systems behind Facebook’s products, create web applications that reach millions of people, build high volume servers and be a part of a team that’s working to help connect people around the globe.
- Develop highly scalable classifiers and tools leveraging machine learning, 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)
- Perform specific responsibilities which vary by team
- PhD in Computer Science, related STEM or quantitative field
- Graduating with a PhD by December 2017, or completing a university postdoctoral assignment
- Research and/or work experience in machine learning, NLP, recommendation systems, pattern recognition, signal processing, data mining, artificial intelligence, information retrieval or computer vision.
- Experience in systems software or algorithms
- Experience in Java or C++, Perl, PHP or Python
- Interpersonal skills, cross-group and cross-culture collaboration
- Problem solving capabilities
- Proven track record of achieving significant results
- Ability to obtain work authorization in the United States in 2017
- Experience with Hadoop/Hbase/Pig or Mapreduce/Sawzall/Bigtable
- Demonstrated software engineer experience via an internship, work experience, coding competitions, or PhD papers
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