How Turing Is Leveraging AI for Matching and Evaluating Developers?

Ankit Sahu

21 Feb 2025•5 mins read

How is Turing leveraging AI for matching and vetting developers?

Turing uses machine learning for vetting and matching.

Turing CEO Jonathan Sidharth says: “At Turing, we have thousands of developers going through our intelligent vetting process. And hence, we have the perfect data set to do supervised machine learning. We have data from our vetting that generates all of these rich input features. Using different algorithms, we are able to tell which developers end up passing our vetting process and which developers end up failing. We can then use this data to predict the probability that a developer would get an answer to a question right that they haven’t even seen yet.”

“This process helps us make our vetting very efficient. Resultantly, a developer doesn’t have to spend a ton of time on Turing developer tests. If they get concepts a, b, and c right, we can predict the probability that they’ll get concepts d and e right. This is a magical experience where we are using machine learning to auto-complete the gaps in the vetting process.” he further adds.

The beauty of this vetting process is it generates input features for our matching and ranking algorithms, similar to how Google uses machine-learned ranking to match a document with a keyword.

Jonathan says, “Turing is also using machine-learned ranking to match a developer’s profile with the job. Here again, we use supervised machine learning. We use gradient booster, decision trees, logistic regression, and a few other techniques to predict the probability whether a developer will be successful in a job with a customer.”

The benefits of matching candidates with AI recommendation engine

1. Remove the issues of keyword searches with contextual evaluation
Companies don’t overlook candidates with AI-based recommendations. Candidates that (A) don’t have aesthetically appealing CVs and/or (B) didn’t grasp the proper keywords from JD but might be a perfect fit, get a highlighted recommendation.

2. Reduce bias and improve the quality of the recruitment process
AI matching can give the hiring process a neutral perspective. Turing’s AI considers a candidate’s complete background and set of talents. The applicant’s age, gender, and race have no bearing on the score.

3. Attract the right candidates and receive fewer irrelevant applications
With Turing’s AI automatically identifying the most suited opportunities for candidates, businesses save time and recruit more effectively.

Bottom line

When emerging technologies increase their capacity to match the appropriate person with the right job, both organizations and people stand to gain significantly.

The best example is Turing’s Intelligent Talent Cloud. The cloud uses machine learning efficiently to source, vet, match, and manage over 3 million developers worldwide. Currently, Turing is helping organizations save valuable time and resources to build their dream engineering team in a matter of days.

FAQs

1. ​​How can AI be used to assess job candidates?
AI will alter the role of the recruiters through artificial intelligence, which will enable recruiters to be more proactive in their hiring, assist in determining whether a candidate fits into the company culture, and improve their relationships with hiring managers. They will also be able to calculate recruitment ROI easily.

2. What is matching in artificial intelligence?
Data is searched, indexed, and retrieved from a database using a variety of artificial intelligence-based data sorting and matching algorithms in a data management technique called intelligent matching.

3. What are the best AI tools for recruiting?
7 best AI recruiting tools

  1. Turing
  2. Arya
  3. Zoho Recruit
  4. Skillate
  5. Talenture
  6. Fetcher
  7. TurboHire

4. Can artificial intelligence be used to make better decisions in hiring decisions?
Applications with artificial intelligence speed up the evaluation of candidates during an interview process. AI-based pre-screening tests eliminate prejudices, lowering the possibility of passing over a good applicant.

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