Technical screens for new grads in 2026: coding rounds, take-homes and AI-allowed interviews

Two years ago the advice for technical interviews fit on an index card: grind algorithm problems, learn to talk while you code, done. In 2026 the card needs a second side. Online assessments come with detailed proctoring, take-homes come with written rules about AI, and a handful of employers now run rounds where you are expected to use an AI assistant and are graded on how well you do it. Most rounds still forbid it. The skill that matters most is the old one, understanding code well enough to judge it, but the formats around it have multiplied.

This guide covers the formats you are likely to meet, how to practice for each, and what the person on the other side is writing down. For where these rounds sit in the overall process, see the new-grad interview loop.

Online assessments: timed, solo, watched

The online assessment (OA) is usually the first technical filter: a few problems against a clock on a platform such as HackerRank or CodeSignal, done alone. Treat it as monitored. HackerRank describes its screening product as combining "advanced AI plagiarism detection, tab- and copy-paste proctoring, multi-monitor detection, and live photo monitoring to flag suspicious activity" (HackerRank). Unless the instructions say otherwise, an AI assistant in another window is exactly the kind of thing that gets flagged.

How to practice.

• Use the platform's own practice mode once, so the editor, the input format and the "run tests" button hold no surprises on the day.
• Do timed sets, not single problems. The skill is triage: read everything first, take the problem you can finish, and come back.
• Read the constraints. An input of a hundred thousand items is telling you a quadratic solution will probably time out.
• Where scoring is by hidden test cases, a correct simple solution beats an ambitious one that fails to run. Get something passing, then improve it.

Live coding: one problem, explained

A live round is an engineer, a shared editor and usually one problem with follow-ups. Correctness matters, but so does everything around it: the questions you ask, the approach you propose before you type, how you test, and how you respond to a hint.

How to practice.

• Work through the common patterns (hash maps, two pointers, sliding windows, stacks, trees and graphs with breadth- and depth-first search, heaps, intervals and basic dynamic programming) until you can recognize which one a problem wants.
• Prefer depth to volume. Solve a problem, then solve it again from scratch a week later without notes. If you can't, you memorized it rather than learned it.
• Practice out loud with another person at least weekly. Narrating is a separate skill from solving, and it improves fastest with an audience.
• Rehearse a routine: restate the problem, ask about edge cases and input size, describe an approach and its complexity, code, trace an example by hand, then discuss improvements.

Take-homes: a small project with rules

A take-home is a scoped project done on your own time, often followed by a conversation where you walk through it. The rules on outside help are written down more often than they used to be, and they differ. Anthropic asks candidates to complete take-homes "without Claude unless we indicate otherwise" (Anthropic). Datadog says a submission "should reflect your original conclusions and perspectives" and that you should expect to explain your work live (Datadog). If the instructions are silent, ask the recruiter in writing before you start, and keep the answer.

How to practice. Build one small project end to end under a time box: a clear README with how to run it, tests for the core logic, and a short section on the decisions you made and what you would do with more time. Reviewers read that section closely, because it shows judgment. Do not exceed the suggested time by days; scope discipline is part of what is being assessed.

System design, lite

Full system design interviews are mostly for experienced hires, but new grads sometimes get a lighter version: design the backend for a simple app, or explain the architecture of a project on your resume. The bar is clear requirements, a sensible API and data model, a simple diagram, and an honest answer about where it would break first.

How to practice. Take your best project and explain its architecture to a friend in ten minutes with a drawing. Then answer three questions: what happens with a hundred times more users, what fails if the database goes down, and what you would cache. If you can do that for your own work, you can do it for an unfamiliar prompt.

Code reading and AI-allowed rounds

A newer format gives you an existing codebase rather than a blank editor, and asks you to read it, find bugs and improve it. At several employers, that format now comes with an AI assistant built in. A few verified examples:

• Canva said in June 2025 that it expects candidates for backend, machine learning and frontend roles to use AI tools such as Copilot, Cursor and Claude in technical interviews. Its problems are "complex, ambiguous, and realistic," and interviewers look at whether candidates "understand when and how to leverage AI effectively" and can "identify and fix issues in AI-generated code" (Canva Engineering).
• Meta told staff in July 2025 that it would let some coding candidates use an AI assistant, according to 404 Media. A spokesperson confirmed to Business Insider that the company was "testing how to provide these tools to applicants during interviews" (Business Insider via Yahoo).
• Google was reported in May 2026 to be piloting an AI assistant (Gemini) in its code comprehension round for early- and mid-career roles on select US teams, starting in the second half of 2026, where "interviewers will evaluate AI fluency, including prompt engineering, output validation, and debugging skills" (Entrepreneur, citing Business Insider).
• Datadog runs AI-assisted coding interviews for certain roles and tells candidates in advance; otherwise, "unless explicitly stated otherwise, you should not use AI tools during live coding interviews or technical assessments" (Datadog).

Assessment platforms have followed. CodeSignal launched AI-assisted assessments in May 2025 with a built-in assistant, and says hiring teams receive "a full transcript of candidate-AI interactions, along with a session replay" (CodeSignal, press release).

Three things are consistent across these. Permission is granted per round, not per company. The assistant is usually provided inside the interview environment rather than brought by you. And your use of it is visible to the interviewer. Meanwhile the default elsewhere has not changed: Amazon's guidelines, as reported by Fortune, say "please do not use gen AI tools during your interview unless explicitly permitted," with possible disqualification for those who do (Fortune). If nobody has told you a round allows AI, it doesn't.

How to practice for AI-allowed rounds.

• Clone a small open-source project, pick an open bug, and fix it with an assistant. The point is reading unfamiliar code fast.
• Write or run tests before accepting any generated change. Output validation is the scored skill, so make it a habit.
• Prompt with specifics (the file, the function, the failing case) rather than pasting the whole task.
• Narrate why you accept or reject a suggestion. An interviewer who sees you catch a confident wrong answer learns more than one who watches you accept a right one.
• Keep doing unassisted practice too. Canva notes that "code fluency and technical depth were still absolute requirements, just evaluated in a different context" (Canva Engineering), and most of your rounds elsewhere will be AI-free.

What interviewers are scoring

Structured interviews are scored against written guides. Google's re:Work guide says Google "often documents with illustrative examples what a poor, borderline, solid, and outstanding answer would cover" for each attribute a question tests (Google re:Work). Rubrics vary, but in technical rounds the attributes usually come down to the same handful:

• Problem understanding: did you clarify before you built?
• Approach: was the choice reasoned, and did you know its cost?
• Code: correct, readable, sensibly named and structured.
• Verification: did you test, find your own bugs, and handle edge cases?
• Communication: could the interviewer follow your thinking, and did you use hints well?
• Judgment with tools (in AI-allowed rounds): did you check what the assistant produced, and stay in charge of the decisions?

Notice that "solved it" is one line, not the whole score. A candidate who finishes in silence gives the interviewer less to write on most of those lines than one who gets most of the way with clear reasoning.

A four-week practice plan

1. Week one: platform familiarity and two timed OA-style sets.
2. Week two: two live mock interviews with a peer, swapping roles. Interviewing someone else teaches you what the rubric sees.
3. Week three: a time-boxed take-home-style project with README and tests, and a ten-minute architecture walkthrough of your best project.
4. Week four: an assisted bug fix in an unfamiliar codebase, followed the same day by an unassisted live problem, so neither skill fades.

If you want feedback on your practice sessions from an AI tool, our guide on practicing interviews with AI, honestly covers where preparation ends and live assistance begins.

A note for international students

Technical rounds are the same whatever your visa status, and interviewers in them are rarely the people who decide sponsorship; that conversation belongs with the recruiter. If the role is an internship while you are still enrolled on F-1, the work needs Curricular Practical Training authorized by your DSO before you start, and our guide to Day-1 CPT and the 2026 crackdown explains the risks of some arrangements. When you are ready to put the practice to use, browse new-grad roles and internships.