Software Engineer, PhD Intern/Co-op (Machine Learning)
Facebook - Menlo Park CA

Internship Category: Paid

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  • 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’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 PhD intern 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 thats working to help connect people around the globe.

    As part of our hiring process, PhD interns are pre-assigned to a relevant team based on their expertise and interests.

    This internship has a minimum twelve (12) week duration with 2018 start dates ONLY.

    Responsibilities:

    • 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

    Mininum Qualifications:

    • Pursuing PhD in Computer Science, related STEM or quantitative field or relevant experience
    • Must be currently enrolled in a full time degree program and returning to the program after the completion of the internship
    • Research experience in a relevant field, such as machine learning, NLP, recommendation systems, pattern recognition, signal processing, data mining, artificial intelligence, or computer vision
    • Experience in systems software or algorithms
    • Experience in C/C++, Java, Perl, or PHP
    • Experience in scripting languages such as Perl, PHP, Python, and shell scripts
    • Experience with Hadoop/Hbase/Pig or Mapreduce/Sawzall/Bigtable is a plus
    • Excellent interpersonal skills, cross-group and cross-culture collaboration
    • High levels of creativity and quick problem solving capabilities
    • Proven track record of achieving significant results
    • Ability to obtain work authorization in the United States in 2018

    Preferred Qualifications:

    • Demonstrated software engineer experience via an internship, work experience, coding competitions, or used contributions in open source repositories (e.g. GitHub)
    • Proven track record of achieving results as demonstrated by grants, fellowships, patents, as well as first-authored publications at workshops or conferences such as ICML, NIPS, KDD or similar
    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’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 PhD intern 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 thats working to help connect people around the globe.

    As part of our hiring process, PhD interns are pre-assigned to a relevant team based on their expertise and interests.

    This internship has a minimum twelve (12) week duration with 2018 start dates ONLY.

    Responsibilities:

    • 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

    Mininum Qualifications:

    • Pursuing PhD in Computer Science, related STEM or quantitative field or relevant experience
    • Must be currently enrolled in a full time degree program and returning to the program after the completion of the internship
    • Research experience in a relevant field, such as machine learning, NLP, recommendation systems, pattern recognition, signal processing, data mining, artificial intelligence, or computer vision
    • Experience in systems software or algorithms
    • Experience in C/C++, Java, Perl, or PHP
    • Experience in scripting languages such as Perl, PHP, Python, and shell scripts
    • Experience with Hadoop/Hbase/Pig or Mapreduce/Sawzall/Bigtable is a plus
    • Excellent interpersonal skills, cross-group and cross-culture collaboration
    • High levels of creativity and quick problem solving capabilities
    • Proven track record of achieving significant results
    • Ability to obtain work authorization in the United States in 2018

    Preferred Qualifications:

    • Demonstrated software engineer experience via an internship, work experience, coding competitions, or used contributions in open source repositories (e.g. GitHub)
    • Proven track record of achieving results as demonstrated by grants, fellowships, patents, as well as first-authored publications at workshops or conferences such as ICML, NIPS, KDD or similar
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