Leveraging Machine Learning for Effective Talent Matching: A Case Study with Turing's Talent Cloud

Turing’s Talent Cloud connects businesses with top-tier software developers from all around the world.  In this study, we provide an overview of how Turing leverages machine learning (ML) to deliver value to both businesses and software developers. Further, we provide a high-level understanding of the foundations our ML models are built on. Finally, we go deeper into one specific case study on how AI has improved the matching process for Turing’s internal hiring team and clients who depend on Turing developers for their engineering efforts.


Turing’s Talent Cloud delivers value to companies looking to hire developers through two key foundational elements: vetting and matching. Machine learning drives their success in the following ways:

  • Vetting (Technical Assessment): Developers typically undergo several hours of technical and non-technical tests and interviews. Our ML models help us recommend the right jobs for these developers based on their skills and competence. Cheating detection algorithms and heuristics help us automatically identify developers who cheated in coding challenges.

  • Matching: Turing’s Talent Cloud has more than two million developers from around the world. Our ML algorithms leverage features extracted from the job requirements and developer profiles to surface the most relevant candidate. This enables clients to interview only the most qualified developers to scale their teams quickly. Further in this study, we will dig deeper into our journey of making these matching algorithms effective.

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