HackerRank launched its automated interviewer platform, Chakra, making the software general availability after six months of private testing. The specialized agent conducts technical interviews for software engineering candidates, running over 500,000 preliminary sessions for enterprise clients like Snowflake, Snorkel, and Capgemini before its public release. By shifting evaluation focus away from simple code generation, Chakra evaluates how candidates collaborate with coding assistants, reason through architecture choices, and defend their technical decisions in real time.
The shift toward testing developer judgment addresses a massive change in software development. As developers routinely generate working code using artificial intelligence, simply producing a functional output no longer proves technical skill. HackerRank co-founder and chief executive officer Vivek Ravisankar noted that because anyone can generate software artifacts now, hiring teams must evaluate whether candidates actually understand the code they accept from automated tools. Chakra tests this capability by placing candidates inside live code repositories with built-in assistants, tracking how candidates frame prompts, evaluate suggestions, and troubleshoot errors.
During a Chakra session, the system asks candidates adaptive follow-up questions based on their live coding decisions. If a developer accepts a complex algorithm generated by an assistant, Chakra probes for technical depth by asking them to explain the underlying logic, time complexity, and security trade-offs. If the candidate cannot defend or explain the code they accepted, the system flags the gap in the final report delivered to hiring managers.
Combining code screenings, technical skill tests, and behavioral interviews into a single automated session speeds up initial hiring pipelines significantly. Enterprise recruiters can streamline three separate evaluation steps into one conversational assessment, freeing engineering teams from spending dozens of hours conducting basic technical screens. Candidates also reported positive experiences during beta testing, giving the platform an average rating of 4.8 out of 5 stars.
Allowing candidates to use external tools during interviews actually reduced cheating flags. Suspicious activity alerts dropped by 70% to 80% compared to traditional closed-book coding tests. Because candidates are expected to use coding assistants naturally, Chakra monitors session integrity by watching for unauthorized secondary devices, off-screen help, or unapproved browser applications rather than penalizing standard tool usage.
To address bias concerns and satisfy strict hiring regulations, HackerRank subjects Chakra to independent third-party bias audits. The platform complies with local regulations such as New York City’s Local Law 144, which mandates regular audit reporting for automated hiring tools. Ravisankar emphasized that properly calibrated software evaluations reduce human bias during initial screening rounds, creating fairer assessments for global candidates.
Deploying autonomous interviewers marks a permanent transformation in how software companies evaluate technical talent. As AI coding assistants handle routine syntax generation, software engineering roles increasingly demand high-level architectural judgment, critical evaluation skills, and clear technical communication. Platforms like Chakra set a new baseline for technical hiring, ensuring that candidates possess the underlying knowledge required to guide automated tools effectively.