HackerRank’s AI interviewer offers a glimpse into what job interviews could become

HackerRank has introduced Chakra, an AI agent designed to conduct and evaluate developer interviews by observing candidates as they work on real-world code tasks. Rather than focusing only on whether a candidate reaches a correct answer, the system monitors the candidate’s approach, asks contextual follow-ups, and scores signals the company describes as critical thinking, judgment and “AI fluency” — how well a candidate frames problems for AI and vets its output. Chakra was tested internally and with customers for about six months before its public rollout. HackerRank reports the tool completed over 500,000 interviews during that period, with companies such as Snowflake, Snorkel and Capgemini among the early testers. The startup, founded in 2012 and backed by Y Combinator, says it now serves more than 3,000 business customers and a developer community exceeding 30 million. HackerRank positions Chakra as a consolidation of recruitment steps that traditionally unfolded across multiple rounds — recruiter screen, take-home assessment and technical follow-up — into a single session that resembles on-the-job work. During an interview, candidates work in a canvas tied to a real code repository and have access to an AI assistant; Chakra uses the working context to probe choices and adapt prompts, then produces a detailed report for hiring managers. The company says the approach reduced suspicious-activity flags compared with its traditional assessments: HackerRank reported flags were 70% to 80% lower in Chakra interviews, though rates varied by geography and seniority. HackerRank’s CEO Vivek Ravisankar also emphasized that Chakra is intended to score and standardize structured evaluations while leaving final hiring decisions to human interviewers. He argued that a well-tuned AI can apply a consistent rubric across candidates. At the same time, the deeper role for AI in hiring raises concerns about bias and oversight. Automated hiring tools can carry or amplify biases from their data and design, and regulators have already started to require audits and candidate notice in some jurisdictions — for example, New York City’s requirement for independent bias audits of certain automated employment tools, a compliance area Ravisankar said HackerRank has built for.