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HackerRank Chakra is a technical interview service that combines hands-on coding in a real-world repository with an AI assistant and contextual follow-up questions. It aims to assess not just the finished code, but how candidates reason, make decisions and use AI. HackerRank says the system generates scores and reports while human hiring teams make the final decision. Whether this approach improves hiring—or produces fair, reliable evaluations—remains an open question.
How a Chakra interview works
Rather than asking a candidate to solve an isolated coding puzzle and submitting only a final answer, Chakra places the candidate in a coding canvas built around a real-world repository. An AI assistant is available in the environment. As the candidate works, the system can ask follow-up questions tied to the task—for example, why they chose a particular approach or how they would adapt if a constraint changed.
That format lets an interviewer examine a candidate’s process alongside the artifact they produce: technical decisions, problem-solving, judgment, communication and use of AI. HackerRank describes the goal as making an interview more like practical work. CEO Vivek Ravisankar explained the motivation to TechCrunch: “The previous modality of evaluation was evaluating the output. Now, because of AI, anybody can produce an artifact.”
According to HackerRank, Chakra scores competencies and provides a rationale supported by interview transcripts and evidence from the candidate’s work. The company says AI generates the scores and reports, while the hiring team retains the final decision. That distinction matters: an AI-generated evaluation can influence a decision even when a person formally makes it.
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What is new—and what is not established
HackerRank reported Chakra’s general availability on October 5, 2026, after about six months in beta. TechCrunch reported that HackerRank said more than 500,000 interviews had taken place during testing, and that Snowflake, Snorkel and Capgemini were among the organizations that tried the product. Those figures and examples were reported as company claims, not as independently audited results.
HackerRank’s product page separately lists an average candidate rating above 4.8 across 500,000-plus interviews. That is also a vendor claim; it should not be treated as an independently verified measure of candidate experience or interview quality.
Ravisankar also told TechCrunch that Chakra interviews produced 70% to 80% fewer suspicious-activity flags than comparable traditional HackerRank assessments. He said the rate varied by geography and seniority. This is a company-reported comparison, not independent evidence that Chakra eliminates cheating or is more accurate at detecting it.
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HackerRank says its approach uses expert rubrics, human annotations, checks of human-AI agreement and frequent third-party bias analyses. These describe the company’s process; they do not, by themselves, prove that the scores are valid, unbiased or predictive of job performance. Ravisankar’s statement that “AI is way less biased than humans, if you tune it properly” is his argument, not an established finding. A consistent rubric can still encode a poor measure or produce uneven results.
How this compares with other interview formats
These formats ask different things of candidates. The distinctions below describe the approaches in the available accounts, not a universal ranking of interview quality.
| Format | What the candidate does | What the evaluation can focus on | What is known about the decision process |
|---|---|---|---|
| Chakra technical interview | Works in a code repository with an AI assistant and responds to contextual follow-ups. | Code and work process, including reasoning, technical choices, communication and AI use, as described by HackerRank. | HackerRank says AI produces scores and reports and human hiring teams make the final decision. |
| Traditional coding assessment | Completes a coding task or assessment; the exact setup varies. | Often emphasizes the submitted output. Ravisankar framed this output-focused approach as the contrast Chakra addresses. | Not stated for traditional assessments generally; processes differ by employer and test. |
| AI voice-agent interview in the 2026 field experiment | Completes an interview with an AI voice agent rather than a human recruiter. | The study examined outcomes in that experiment; its abstract does not establish that the interviews evaluated coding or the same competencies as Chakra. | The working paper says human recruiters evaluated interviews and made hiring decisions. |
What independent research can—and cannot—tell us
A 2026 working paper by Brian Jabarian and Luca Henkel reports a natural field experiment involving 70,000 applicants assigned to AI voice-agent or human-recruiter interviews. The authors found that applicants interviewed by AI agents were 12% more likely to receive job offers, with no decline in productivity among hired workers. Human recruiters evaluated interviews and made the hiring decisions, according to the paper’s abstract.
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This is useful context for the broader question of AI in hiring, but it is not a study of Chakra. It concerns voice-agent interviews at the firms in that experiment, not hands-on technical interviews in HackerRank’s coding environment. Its result cannot establish that Chakra changes offer rates, improves hiring or predicts performance.
Candidate data, notice and oversight
HackerRank’s candidate notice says employers may use AI features to evaluate performance and participation integrity, conduct autonomous interviews and follow-up questions, and assess skills such as coding, problem-solving, communication, work patterns, rule adherence and AI fluency. Depending on the feature, processing may include webcam images or other signals. Candidates should read the notice and the employer’s instructions for the specific interview rather than assume every Chakra interview collects the same information.
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Rules for employers also vary by location. New York City’s Department of Consumer and Worker Protection says Local Law 144 bars covered employers and employment agencies from using a covered automated employment decision tool unless it has undergone a bias audit within one year of use, audit information is publicly available, and required notices are provided. Whether a particular interview tool and use are covered depends on the law’s scope. This is not a rule that applies to all AI interviews or all locations.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Questions candidates can ask before the interview
Because interview setups and applicable rules differ, candidates can ask the employer or recruiter for specifics before starting:
- What parts of the interview are evaluated by AI, and what criteria or competencies are being assessed?
- Is an AI assistant available, and are candidates expected or permitted to use it? Is its use recorded or evaluated?
- Which work artifacts, transcripts, webcam images or other signals are collected, and how are they used and retained?
- Does a person review the AI-generated score or report, and who makes the hiring decision?
- Is an accommodation or alternative interview process available, and how can it be requested?
- What options exist to request a correction, human review or an explanation of AI use if the outcome is adverse?
For employers comparing interview approaches, the useful questions are similarly concrete: Does the role call for a voice conversation or a hands-on task? Should evaluation focus on a final answer or on observed work process? What AI assistance is allowed, and what is recorded? How does a human review the evidence? What notices, audits, privacy protections and accommodation routes apply?
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What Chakra signals about the future of interviews
Chakra points toward a move from evaluating only a polished output to examining how someone reaches it, including how they use AI. That may make technical interviews feel closer to everyday work, but it also gives employers more process data to interpret. The format’s promise therefore depends on more than a convincing demo: the task must measure skills that matter for the job, the scoring must be explainable and appropriately validated, and candidates need clear notice and meaningful human oversight.
Ravisankar called Chakra “the headline” and “the way that we’re going to move forward,” describing HackerRank’s strategy rather than an independent forecast. The product is a glimpse of one possible interview model, not proof that AI-led evaluation is already fairer or more effective than other approaches.
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