Machine Learning Engineer – Search & Recommendations (all levels)

Website Twitter


San Francisco, New York City, Atlanta

This role accepts applications for work in the locations as noted above. Roles listing ‘Remote US’ as a location are not currently available in the following states: Colorado, Iowa, and Louisiana.

Company description

Twitter is what’s happening and what people are talking about right now. For us, life’s not about a job, it’s about purpose. We believe real change starts with conversation. Here, your voice matters. Come as you are and together we’ll do what’s right (not what’s easy) to serve the public conversation.

Job description

Who We Are: Twitter is serving the public conversation, and conversations are happening on Twitter every day about every subject and any event. The Search and Recommendations teams job is to connect our users to the conversations and people that are relevant to them.

Search and Recommendations builds infrastructure and models to support this mission across multiple product areas. We are responsible for the recommendations you see under Search, Explore, Trends, Topics, the Home Timeline. The unrivaled challenges that we face at Twitter are both the data scale and the real-time nature of the product. How do you find the most meaningful content among hundreds of millions of new tweets for hundreds of millions of users every day at Twitter? We build large scale personalized recommendation engines utilizing different kinds of signals such as social network, user activity, and geolocation. We work on machine learning, trend detection, search understanding and retrieval, graph algorithms, recommendation systems, distributed systems, and social graph analysis.

What You’ll Do:

  • Improve existing search engine and recommendation systems, experiment with new directions and provide ML solutions in recommendation systems within Twitter.
  • Build models and algorithms to understand user interest, user intent, and improve content relevancy.
  • Build features and develop new ranking algorithms.
  • Work closely with live production systems and product teams, and deliver ML solutions at scale within the Twitter tech stack.


Who you are: You are a machine learning software engineer with a passion for working on exciting algorithmic and deep infrastructure issues in ML environments. More specifically, you are doing the following kind of Machine Learning work:

  • Thrive on working in concert with other smart people, including from distributed offices.
  • Communicate fluidly, at the level of your audience, and seek to understand and be understood.
  • Have the ability to take on complex problems, learn quickly, iterate, and persist towards a good solution.
  • Take pride in polishing and supporting our products.
  • In the role, you are employing a basic understanding of one or more of these concepts: Information Retrieval, Recommendation Systems, Social Network Analysis.
  • You regularly verify the performance & correctness of the implementations of ML techniques. You are able to triage and fix bugs/issues when they arise.


We are currently looking for candidates across the board, from University graduates with a BS, MS or PhD in Computer Science, to Staff level candidates with 5+ years of experience.

  • Fluent in one or more languages like Java, Scala, C++, Python
  • Experience with offline and online data processing frameworks
  • Knowledgeable of core CS concepts such as common data structures and algorithms
  • Comfortable conducting design and code reviews

Additional information

We are committed to an inclusive and diverse Twitter. Twitter is an equal opportunity employer. We do not discriminate based on race, ethnicity, color, ancestry, national origin, religion, sex, sexual orientation, gender identity, age, disability, veteran, genetic information, marital status or any other legally protected status.

San Francisco applicants: Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

To apply for this job please visit