What happens when AI moves from helping job candidates to evaluating them? HackerRank , a platform used by companies to assess and hire developers, is offering a glimpse at what that future of job interviews could look like with Chakra , an AI agent that conducts interviews, observes candidates as they work, and evaluates not just their answers but also how they got there. After around six months in beta, HackerRank is making Chakra generally available to its customers on Monday.
The startup says the AI interviewer has already conducted more than 500,000 interviews during testing, with companies including Snowflake, Snorkel, and Capgemini among those that tried it, while HackerRank also tested the product internally. AI has been a staple in job interviews for some time , with companies using voice agents and other automated tools to screen candidates and make the hiring process more efficient. Job seekers, meanwhile, have increasingly gained their own AI tools to help them navigate interviews, sometimes without employers knowing.
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.