What happens when AI moves from helping job candidates to evaluating them?

 

HackerRank is offering a glimpse into the future of job interviews with Chakra, an AI interviewer that conducts interviews, observes candidates as they work, and evaluates not only their answers but also how they arrive at them.

 

After roughly six months in beta, HackerRank is making Chakra generally available to customers. The company says the AI interviewer has conducted more than 500,000 interviews during testing, with companies including Snowflake, Snorkel, and Capgemini among those that have tested the platform.

 

AI has already become part of the hiring process, with companies using voice agents and automated tools to screen candidates and streamline recruitment. At the same time, job seekers are increasingly turning to AI tools to help them during interviews, sometimes without employers knowing.

 

With Chakra, HackerRank is betting that AI can transform not only how interviews are conducted but also what employers can evaluate. Instead of focusing solely on whether a candidate reaches the correct answer, the platform aims to measure harder-to-capture qualities such as critical thinking, judgment, and “AI fluency” — including how effectively candidates frame problems for AI, evaluate its output, and guide it toward a solution.

 

HackerRank co-founder and CEO Vivek Ravisankar says the traditional approach focused largely on evaluating the final output. But with AI making it easier for almost anyone to produce an artifact, employers increasingly need to understand the thinking and judgment behind that result.

 

In practice, Chakra is designed to make interviews resemble real-world work rather than traditional coding tests. Candidates receive a task involving a real code repository and work through it in a workspace that includes an AI assistant. As they work, Chakra can use the context of their actions to ask follow-up questions — such as why they selected a particular approach or how they would adapt their solution when a new constraint is introduced.

 

HackerRank says Chakra can also consolidate multiple stages of the traditional hiring process into a single interview. Instead of having a recruiter screen, take-home assessment, and follow-up technical interview, companies can potentially combine these steps into one AI-powered session.

 

Giving candidates access to AI during an interview might appear to make cheating easier. However, HackerRank says it observed the opposite during testing. The company reported that suspicious-activity flags were 70% to 80% lower in Chakra interviews than in comparable traditional assessments, although the results varied based on factors such as geography and seniority.

 

The company believes that allowing candidates to use AI openly reduces the incentive to secretly rely on outside tools that provide answers during an interview.

 

HackerRank has spent years helping companies evaluate developers through coding challenges. The platform now serves more than 3,000 business customers, including Amazon, Nvidia, Clay, and Replit, and has a global community of more than 30 million developers.

 

Chakra represents a significant shift from HackerRank’s traditional assessment model. Instead of simply testing whether developers can solve coding problems correctly, the company is moving toward evaluating how candidates think, collaborate with AI, and make decisions in realistic work environments.

 

Ravisankar has compared this transition to Apple’s move from the iPod to the iPhone: the existing product still has value, but the newer technology represents where the market is heading.

 

Still, giving AI a larger role in evaluating candidates raises important questions about how much of a hiring decision should be delegated to algorithms.

 

HackerRank says Chakra is designed to score candidates rather than make the final hiring decision. Human interviewers remain responsible for determining whether a candidate is the right fit for the company, team, and role.

 

AI can potentially bring greater consistency by applying the same evaluation criteria to every candidate. But consistency does not automatically eliminate bias. Automated hiring systems can inherit or amplify biases present in their training data, models, or evaluation criteria.

 

That has already attracted regulatory attention. New York City, for example, requires certain automated employment decision tools to undergo independent bias audits and requires employers to provide notice to candidates before using them.

 

As AI becomes increasingly involved in hiring, tools like Chakra could fundamentally change the interview process — shifting the focus from “Can you solve this problem?” to “How do you think, make decisions, and work with AI to solve it?”