Staff Machine Learning Engineer - Health ML

Staff Machine Learning Engineer - Health ML

This job has closed but is shown for context on data science work at Twitter.

Are you an engineer who’s interested in tackling very challenging adversarial problems and passionate about defending online users against abuse, spam, and manipulation? Do you love working on challenging problems that require a multi-disciplinary approach, creative solutions, and rapid product iterations? Will you be proud to work on a real-time, scalable system that serves millions of users daily? If so, you should join us.


Who We Are
The Health ML engineering team is responsible for building scalable detection systems that keep spam, manipulation, and abuse at bay. We use ML and relevance techniques to make Twitter safer and to limit the spread of misinformation on the platform. Our team works across the product to detect abusive and spammy users and content, increase action on bad actors, drive changes in user behavior, and detect and remediate accounts that are violating the terms of service on Twitter.

We develop, maintain, and contribute to several machine learning models and systems, including:

  • Models that detect unwanted interactions
  • Models to prioritize human review of accounts violating Twitter's policies to more quickly take action and limit their damage
  • Detection of bots that misuse the platform or spread misinformation
  • Detection of repeat abusive offenders who create new accounts after being suspended
  • Real-time rule engines and clustering systems to identify and action on clusters of bad actors at scale


What You’ll Do
Although you will work on cutting-edge problems, this position is not a pure research position. You will participate in the engineering life-cycle at Twitter, including designing distributed systems, writing production code and data pipelines, conducting code reviews and working alongside our infrastructure and reliability teams. You’ll apply data science, machine learning, and/or graph analysis techniques to a variety of modeling and relevance problems involving users, their social graph, their tweets, and their behavior.

This job has closed but is shown for context on data science work at Twitter.
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