: Senior Staff Applied Machine Learning Researcher
141 Portland St, 6th Floor
Cambridge, MA 02139

Who We Are:

Our mission at Twitter Cortex Applied Research is to empower and guide Twitter’s product teams to help tackle customer problems using Machine Learning methodologies. We are a centralized research team that partners with the product teams to identify product areas where new ML modeling approaches can generate outsized value, and then closely collaborate with them to explore such solutions.

We use machine learning (deep learning and traditional methods), statistical modeling, clustering, time series modeling, and many other analytical techniques to process very large-scale datasets. We do product-focused research, build prototypes, prove their offline efficiency, and work together with the product teams to push them to production. Here are some public examples of recent work at Twitter related to what we work on:

What You’ll Do:

Apply your research expertise to help direct our product modeling efforts, focusing on using creative data-driven ML approaches to solving open-ended product questions. Help us develop new solutions and unlock new directions, as well as analyze and improve the systems we already have. You’ll work closely with ML engineers and data scientists and mentor them on modern approaches to using ML for solving product challenges at Twitter’s scale. You will be collaborating on strategic decisions and future roadmaps for Machine Learning driven products and technologies at Twitter. You are a key member of the Cortex Boston Product Modeling team, which consists of authorities in ML, data science, and software engineering. Your impact will directly affect millions of Twitter users around the globe.

Who You Are:

  • Verifiable, cutting edge research experience
  • You have strong product understanding and an intuition for how to use modeling to address product needs.
  • You adept at digging into new problem domains, applying a wide variety of tools for diverse problems.
  • You communicate well with partners, turning data findings into actionable insights that can direct our modeling efforts.
  • Self-starter who can own complex projects from start to finish.


  • PhD in Computer Science or similar field
  • 10 years of experience with subject expertise in ML
  • 3+ years of independent research career, e.g. as faculty in a university or senior researcher in the industry
  • Ability to inspire, influence and mentor people around you.
  • Evidence of independence, originality and creativity in research
  • Excellent publication record in top conferences either on the theoretical (NeurIPS, ICML, ICLR) or the more applied side (Recsys, ACL, KDD, WSDM, WWW)
  • Recognition in the research community, as evidenced by e.g. invited keynote talks, programme committee or editorial board membership
  • Strong hands-on proficiency with at least one data science programming language, with preference to Python over R
  • Experience coding in Java, Scala, or C++ or any other strongly typed programming language
  • Firm grasp of CS fundamentals, Data structures, and algorithms
  • Experience handling large scale quantitative customer data to tackle problems and answer questions
  • Experience in building ML models
  • Experience with data visualization
  • Experience with social network data a plus

We are committed to diversity and inclusion at Twitter. Twitter is an equal opportunity employer.

We do not discriminate based on race, color, ethnicity, ancestry, national origin, religion, sex, gender, gender identity, gender expression, sexual orientation, age, disability, veteran status, genetic information, marital status or any 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.



Hiring Process

Step 1

After you apply, a recruiter may reach out to you for an introductory call.

Step 2

If your background is a match for the role, you may phone interview with 1-2 people.

Step 3

If you continue through the process, you will come onsite 1-2 times to interview with a total of 5-10 people.