Data Engineering Content Ingestion LMS Engineering Native Video Gateway as a Platform News and Editorial Voice Personalized INTelligence SRE

The mission of the NYC applied machine learning team is to establish and improve the quality, relevance, and richness of LinkedIn’s Economic Graph data, with the aim of creating economic opportunity for LinkedIn members. For example, some of the challenges we tackle are: how to help sales professionals prospect for people (leads) and companies (accounts); how to help people write good resumes; how to identify merger-and-acquisitions, funding events from text and use them to help recruiting and sales professionals do a better job.

In short, we turn LinkedIn business needs into data-driven machine learning problems. They can be broadly categorized as the following groups:

  • Information extraction:
    LinkedIn products require an understanding of the activities of members and organizations. e.g., when a member makes a career move; when a company is involved in a merger-and-acquisition event, or a start-up received funding. Towards this goal, we ingest timely news reports or RSS feeds, and extract these events with many state-of-the-art machine learning methods such as deep-learning-based sequence labeling and conditional-random-fields. Though information extraction is a classical natural language processing (NLP) research problem, we do it with the very practical goal that the output can be directly deployed in LinkedIn products and is beneficial to LinkedIn users.
  • Entity resolution:
    Associating mentions of people and organizations with their canonical LinkedIn id is a very useful application at LinkedIn.  We explore both unsupervised and supervised learning techniques to find the best solution for this task.
  • Recommendation systems (ranking):
    For example, the LinkedIn Resume Assistant helps MS Word users write better resumes by showing them examples of well written work experiences other people have written for similar positions.  The role of our team was to train a model to select the best work experiences, from millions of examples, to surface in the product.  In this ranking task, the goal was to rank work experience snippets, such that the most well-written, appropriate snippets, and those were ranked at the top of the list. We also work on feed ranking tasks, in particular for Linkedin Company pages.