Senior Machine Learning Engineer - Product Modeling


Who we are

Twitter has become the infrastructure of the world’s public conversation. Events break on Twitter, from massive and world-changing to the local data science meetup. Users exchange and shape their thoughts by forming communities with shared interests.

The Applied Research team thrives to understand, document and explain these phenomenons through rigorous data analysis in order to find disruptive growth opportunities in better serving our users.

We use machine learning, statistical modeling, data mining, time series modeling, and many other analytical techniques on the petabytes of data our users generate every month. We build prototypes and work with the product teams to perform experiments — all applied at the scale of Twitter.

What you’ll do

You will work as an integral part of our data science team to develop and implement models, algorithms, and systems that can be applied at scale to Twitter data. Examples of the kind of work you might do include building deep neural networks to classify Twitter users and content, large-scale graph clustering and matrix factorization, recommendation algorithms, or time-series models on user log data. You’ll work with both engineers and data scientists on the team and elsewhere at Twitter.

Who you are:

You probably have

  • Several years of experience working in machine learning
  • A strong interest and experience in machine learning, including deep learning, building classifiers, clustering in high dimension, pattern recognition algorithms, and recommender systems
  • Experience with large datasets and modern data processing systems like Hadoop, Spark, Hive, and Presto
  • Good communication skills, a pragmatic approach to problem solving, and a strong quantitative background
  • Some experience with software engineering best practices (e.g., unit testing, code reviews, design documentation)
  • Knowledgeable of core CS concepts such as data structures, algorithms, and optimization
  • Publications in top conferences is a plus (e.g., ICLR, NIPS, ICML, KDD)


  • An advanced degree (masters, PhD) in machine learning or related field with coursework in machine learning or equivalent work experience
  • Strong software development experience (e.g., Scala, Java, C++, etc.)
  • Strong machine learning/data analysis experience (e.g., Python, R, Matlab, etc.)
  • Familiarity with one or more Deep Learning frameworks (e.g., TensorFlow, Torch, etc.)
  • 3+ years of work experience in machine learning, artificial intelligence, statistics, or related fields

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.


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