Machine Learning Engineer (Natural Language)

Develop natural language technologies that are used by millions every day. 

At Duolingo, we are transforming the way people learn. We have revolutionized language learning for more than 200 million people around the world, and we are looking for talented people with creative, interdisciplinary ideas to help us deliver high-quality education tools to anyone, anywhere, through technology. As an ML Engineer, you will apply your background in machine learning and natural language processing to build product features that leverage state-of-the-art methods in classification and regression, personalization, recommendation, information extraction, parsing, and so on. You will work with a diverse team to come up with new and interesting hypotheses, test them, and then scale them up to data sets with billions of data points unlike what you’ll see anywhere else. 

  • Apply ML and NLP methods to massive data sets
  • Prototype new models, evaluate with small scale experiments, and productionize solutions at scale to millions of active users
  • Work with a cross-functional team of award-winning engineers, researchers, designers, and others to build new product features
  • Iterate on intelligent product quality through continuous A/B testing

  • Graduate-level expertise (or equivalent industry experience) in machine learning, natural language processing, or related field
  • Experience implementing ML & NLP systems at scale in Python, Java, Scala, or C/C++ (i.e., not just R or MATLAB)
  • A strong mathematical background in statistics and machine learning
  • Great presentation and communication skills
  • Ability to relocate to Pittsburgh, PA

  • MS or PhD in machine learning, natural language processing, or related field
  • Previous experience building functional NLU, IR, SMT, dialog, or recommender systems (e.g., commercial products or government projects)
  • A portfolio of relevant publications or open-source projects to share with us
  • A desire to keep up with the field by attending or publishing at relevant conferences (ACL, EMNLP, NAACL-HLT, ICML, NIPS, etc.)

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