HackerRank Opens Its AI Interviewer Chakra to All Customers

The product enters a hiring market where AI already plays a role. Companies have used voice agents and other automated tools to screen candidates and make hiring more efficient, while job seekers have increasingly gained their own AI tools to navigate interviews, sometimes without employers knowing, TechCrunch reported.

With Chakra, HackerRank is betting AI can change not only how interviews are conducted but also what employers can measure. Beyond whether someone arrives at the right answer, the startup wants to assess harder-to-capture signals such as critical thinking and judgment, as well as what it calls “AI fluency” — how well a candidate frames a problem for AI, judges its output, and steers it toward a solution.

“The previous modality of evaluation was evaluating the output,” HackerRank co-founder and CEO Vivek Ravisankar said in an interview. “Now, because of AI, anybody can produce an artifact.” The question for employers, he said, becomes whether they can understand the thinking and judgment that went into producing it.

In practice, a Chakra interview is designed to look more like doing the job than taking a traditional coding test. A candidate gets a task involving a real-world code repository, and they work through it in a canvas that includes an AI assistant. As the candidate works, Chakra can use the context of what they are doing to ask follow-up questions, such as why they chose one approach over another, or how their solution would change if a new constraint were introduced.

Ravisankar told TechCrunch that Chakra is changing the basic structure of the hiring process itself. What previously involved three separate rounds — a recruiter screen, a take-home assessment and a follow-up interview with an engineer — is now combined into a single Chakra interview, he said.

Giving candidates access to AI during an interview might seem to make it easier to cheat. HackerRank says it found the opposite. Suspicious-activity flags were 70% to 80% lower in Chakra interviews than in comparable traditional HackerRank assessments, Ravisankar said, though the rate varied depending on factors such as geography and seniority. He said giving candidates access to AI reduces the incentive to secretly use outside tools that can feed them answers during an interview.

HackerRank launched at TechCrunch Disrupt in 2012 and built its business around coding challenges, eventually helping companies assess and hire developers based on their technical skills. The Y Combinator-backed startup now has more than 3,000 business customers, including Amazon, Nvidia, Clay and Replit, and a community of over 30 million developers worldwide.

Chakra represents a bet against the kind of technical assessment business HackerRank spent years building. Its traditional product largely tested whether developers could solve coding problems correctly. Ravisankar believes AI has made HackerRank’s earlier model less useful to measure engineering ability. He compared the transition internally to Apple moving from the iPod to the iPhone, as the old product still has value, but the new one represents where the startup believes the market is headed. “Chakra is going to be the headline,” he said. “It’s going to be the way that we’re going to move forward.”

Giving AI a deeper role in evaluating candidates raises questions about how much of a hiring decision companies should delegate to an algorithm. Ravisankar told TechCrunch that Chakra is designed to score candidates rather than make the final hiring decision, which remains with humans. AI, he said, can handle more structured parts of an interview by consistently applying criteria set by an employer, while human interviewers can spend more time determining whether they actually want to work with a candidate and answering questions about the company, team and role. “AI is way less biased than humans, if you tune it properly,” Ravisankar said, arguing that an AI system can be instructed to follow the same rubric for every candidate rather than being influenced by factors such as a candidate’s background or education.