Most live interview tools were built before developers used AI to code. HackerRank Interview was built assuming they do. Candidates land in a real codebase with an agentic IDE, the latest AI models, terminal access, and file navigation.
A full development environment launches at the start of every interview. Candidates and your team code side by side the way they would on the job. No awkward screen sharing.

The IDE mirrors a modern developer setup, with inline assistance and a chat panel, so that you can can see how a candidate works alongside AI. Interviewers can monitor AI-candidate interactions in real time, and conversations are also captured in interview reports.

Scorecard Assist auto-fills your interview rubric using transcripts, code, and test cases, so interviewers spend their time interviewing, not writing notes. Move beyond evaluating just code correctness to a holistic framework that looks at code quality, ability to work with AI, code review, and more.
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Integrity signals notify interviewers when the system detects meaningful clusters of suspicious candidate activity, such as multiple copy-paste actions, frequent window resizing, and switching tabs.
The Screen-to-Interview Identity Match verifies that the candidate who completed the screening test is the same individual attending the interview. It uses facial recognition technology to automatically compare images from the test and interview to detect potential identity mismatches.
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“HackerRank is a great tool for interviewing folks consistently, evaluating code live, and collaborating effectively with candidates during the process.”
Find answers to common questions about HackerRank Interview.
HackerRank Interview is a purpose-built live coding interview platform. Not a video call with a screen share layered on top. Tools like Zoom and Teams provide video sharing, but they were never designed for technical hiring. HackerRank Interview gives candidates and interviewers a fully collaborative environment with a shared IDE, real code repositories, a virtual whiteboard, integrity monitoring, and auto-filled scorecards — all in the browser with no downloads required. The result is a faster, fairer, and more signal-rich interview than any screen sharing setup can provide.
The candidate is dropped into a fully functional environment: a real codebase, an agentic IDE with access to the latest AI models, terminal access, and file navigation. From there, they work through tasks that mirror what they'd actually do on the job including building a feature from a PRD, fixing a bug raised in a Jira ticket, reviewing code before merging to master, or building scalable systems. Interviewers code alongside them in real time, observing not just what the candidate produces but how they think, navigate, and work with AI.
Code repositories come with a built-in AI assistant, just like today's developers use in their daily workflows, allowing hiring managers to evaluate how candidates interact with AI tools and offering a clear picture of their ability to integrate these technologies productively. Interviewers can monitor AI-candidate interactions in real time, and conversations are also captured in interview reports — so the evaluation goes beyond code correctness to include AI fluency, judgment, and critical reasoning.
Scorecard Assist auto-fills your interview rubric using transcripts, code, and test cases, so interviewers spend their time interviewing, not writing notes. It moves evaluation beyond code correctness to a holistic framework that captures code quality, ability to work with AI, code review skills, and more. Interview standardization ensures a fair and reliable process for all candidates by creating templates and scorecards, so that the same questions are asked and everyone is measured on the same scale.
Integrity signals notify interviewers when the system detects meaningful clusters of suspicious candidate activity, such as multiple copy-paste actions, frequent window resizing, and switching tabs. HackerRank alerts interviewers of tab switches, code copy-pasting, multiple monitor use, and help from any third-party tools — yes, even the ones who claim to be invisible. Integrity monitoring runs passively in the background so interviewers stay focused on the conversation, not policing behavior.
The Screen-to-Interview Identity Match verifies that the candidate who completed the screening test is the same individual attending the interview. It uses facial recognition technology to automatically compare images from the test and interview to detect potential identity mismatches. This is a critical safeguard as impersonation cases increase. Integrity challenges have grown significantly more complex, with new tools designed explicitly to help candidates cheat appearing almost weekly, and a notable increase in impersonation cases.
HackerRank Screen is a take-home technical assessment platform used at the top of the funnel. Candidates complete it on their own time and results are automatically scored, letting recruiting teams identify the strongest applicants before investing interview time. HackerRank Interview is the next stage: a live, collaborative coding environment where interviewers and candidates work together in real time inside a real codebase. The hiring leaders getting this right have moved from algorithm-centric tasks to real-world repository-based problems putting candidates inside actual code environments and asking them to do what they'd do on day one. Screen filters the funnel; Interview validates the finalist.
Most interview processes are still asking candidates to implement Dijkstra's algorithm on a whiteboard, testing for memorization of algorithms that AI can generate in seconds. A senior engineer on day one opens VS Code with Copilot or Claude, picks up a vague Jira ticket, uses AI to scaffold the boilerplate, critically evaluates what the AI produced, identifies where it's wrong or incomplete, and writes the hard parts themselves. HackerRank Interview is built to test exactly those skills — AI fluency, code quality, critical reasoning, and real-world problem solving — in an environment that mirrors how developers actually work.