HackerRank AI interviewer is live: how to prep

The HackerRank AI interviewer, Chakra, is now generally available. Chakra merges three rounds into one sitting and scores only what lands in the transcript.

So practice narrating tradeoffs and AI use, and save that sitting for roles you approved in Prepin.

One sitting now carries the weight of three. If your reasoning stays in your head, the scorer can't quote it. Here's what changed, and how to prep for it.

What did HackerRank launch with Chakra?

HackerRank made Chakra, its AI interviewer for repository-based coding interviews, generally available to customers after about six months in beta, TechCrunch reports. During testing, Chakra ran more than 500,000 interviews. Snowflake, Snorkel and Capgemini were among the companies that tried it. HackerRank has more than 3,000 business customers.

The format is the bigger story. As TechCrunch put it, "What previously involved three separate rounds, comprising a recruiter screen, take-home assessment, and follow-up interview with an engineer, is now combined into a single Chakra interview." You work on a task inside a real-world code repository, in a canvas that includes an AI assistant. Chakra then asks why you chose one approach over another, or how your solution would change with a new constraint.

How does Chakra score your interview?

Chakra scores on a 0 to 3 scale, and per HackerRank's own explainer, "every score traces back to a specific, verbatim moment in the interview transcript." HackerRank calls this evidence-anchored scoring. Evidence-anchored scoring is a grading method that only credits skills a candidate showed on the record.

Look hard at the bottom row. A 0 Not Assessed means the scorer found no relevant transcript moment at all. Quiet competence lands in that row. If you refactored a module well and never said why, there's no moment to anchor a 3.

Why does one merged round raise the stakes for senior engineers?

Because the recruiter screen, the take-home and the engineer follow-up now happen in one session, a weak sitting can't be rescued by a strong round later. There's no later. HackerRank CEO Vivek Ravisankar told TechCrunch, "Now, because of AI, anybody can produce an artifact." Working code is table stakes, and the reasoning you say out loud is the scored part.

For senior engineers this cuts both ways. Years of design reviews give you plenty to say about tradeoffs. They also build a habit of skipping obvious steps, and an evidence-anchored scorer can't credit a step you never said. That makes every Chakra session a full-weight first round.

How should you practice for a Chakra interview?

Practice by narrating out loud while you change code you didn't write, with an AI assistant open, and record the whole thing. Then grade the transcript the way Chakra would.

1. Clone an unfamiliar open-source repo and give yourself one realistic change, like a bug fix or a small feature.
2. Say what you think the code does and where your change belongs before you edit anything. A plan you kept in your head can't anchor a score.
3. Name two approaches out loud and pick one with a reason, like reads dominate, so I'll cache here.
4. Use the assistant on the record. Say what you asked for, what you threw out, and why.
5. Have a friend add a new constraint halfway through, then talk through how the design changes.
6. Transcribe the recording and score yourself with the table above. Anything you never said out loud is a 0.

If you're picking a format for those reps, we compared AI mocks with peer mocks and paid coaches in a separate guide. Whichever you pick, do the system design reps out loud. A diagram you never describe gives the scorer nothing to quote.

What does AI fluency sound like on the record?

AI fluency is a scoring category that HackerRank describes as "how well a candidate frames a problem for AI, judges its output, and steers it toward a solution." Each of those verbs needs a sentence you say out loud. Accepting a suggestion silently reads the same in a transcript as accepting it blindly.

• Framing out loud means giving the assistant the constraint before the request, like keep the public API stable.
• When judging, name what you checked, like the tests or the null case it missed.
• Steering is saying what you changed and why. Even too clever, give me the boring version counts.

Rejecting a bad suggestion, with a reason, is about the best evidence of judgment you can hand this scorer. Don't hide it.

What does Chakra monitor during the session?

HackerRank's release notes say Chakra detects an additional monitor, screen sharing or exiting full-screen, and pauses the interview. A desktop app locks the screen and restricts unauthorized applications. Employers get reports with full session video playback of your screen, your webcam feed and the transcript.

Practice in the setup you'll test in. One monitor, full-screen, notifications off, and only the AI assistant built into the canvas. TechCrunch reports suspicious-activity flags ran 70% to 80% lower than in comparable traditional HackerRank assessments.

Is an AI interviewer fair to candidates?

Chakra "is designed to score candidates rather than make the final hiring decision, which remains with humans." Ravisankar argues that "AI is way less biased than humans, if you tune it properly." Note the last clause. In the US, New York City requires employers using certain automated employment decision tools to get an independent bias audit and notify candidates, as TechCrunch notes.

An automated employment decision tool is a software tool that scores or ranks candidates to help an employer decide. You can't audit the scorer yourself. You can control what ends up in its transcript.

Which roles deserve a full-weight first round?

Only roles you'd actually accept. If one sitting now replaces a recruiter screen, a take-home and an engineer interview, spraying applications means prepping graded sessions for jobs you never wanted. A candidate agent is an AI tool that works for the job seeker, finding fits first so your graded sessions go to roles worth them.

Prepin's agent turns one voice conversation about your goals, strengths and dealbreakers, plus your LinkedIn and resume, into one structured profile. You set a location mode (remote, hybrid, onsite or specific cities) and a compensation floor once, then review matched roles in an inbox that shows company, location and posted compensation range. It works best for finding 3 to 5 strong fits, and every employer introduction waits for your approval. Product details current as of 2026-10-07.

If you're still employed, approval-first intros keep your profile off desks you didn't pick, which is the point of a quiet search for senior engineers. Weighing tools first? Here's how Prepin compares on job matching.

For the reps themselves, Prepin's interview practice covers coding, system design and behavioral rounds, and the agent flags what's weak in your profile before recruiters see it. Start the voice conversation at prepin.ai. Prepin never auto-applies or sends your profile without your permission.