: Research Scientist, Machine Learning (PhD)

Intro

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.

Summary

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 Research Scientist at Facebook, you will help build 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.

Required Skills

  • 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
Full-time

Employee Testimonials

Edward Fagin (Photo credit: Ryan Mack)
Edward Fagin
Engineering Manager

I'm an engineering manager on our real-time infrastructure team, and my team focuses on GraphQL Subscriptions — a component of the widely-used GraphQL open source query language for APIs — that makes it easier for engineers to build real-time features into their products. At Facebook, this framework powers a number of features that people use every day, including comment-typing indicators and streaming reactions on live videos.

At Facebook, you have the opportunity to contribute to an incredibly wide range of infrastructure projects. While our engineers have a variety of expertise, they share a passion for solving complex engineering challenges at scale. It's humbling to know that everything you work on can potentially impact more than two billion people around the world.

Vaneeta Singh (Photo credit: Ryan Mack)
Vaneeta Singh
Engineering Manager

We develop the network software for Facebook applications on iOS and Android platforms. This includes measuring and improving performance, reliability, security, and efficiency of network communication with Facebook servers. Getting the most out of a diverse set of worldwide mobile networks, and millions of handsets with a wide range of hardware capabilities is challenging as well as fun. All this while we play good citizens in these networks and are mindful of the device battery, storage, and data consumption.