What is an AI Coding Interview?
An AI coding interview is a new technical interview format where the goal is not simply to hand-write a clean solution from scratch. You are given an editor with an AI chat on the side, closer to how many engineers work today with tools like Cursor or Copilot Chat. The key difference is that the interview is still evaluating your technical judgment. The AI is a tool, and you need to show you can leverage it but not overly reliant on it.
If you are preparing for an AI coding interview, this is the format companies mean: an interview where you can use AI, but you are still responsible for the algorithm, correctness, tradeoffs, and final code quality.
In practice, this means you need to operate more like a tech lead than a pure coder. The important skill is not "can the model write code?" It is "can you understand the problem, guide the model well, inspect the output, catch mistakes, and drive the code to the correct behavior?" The point is not to offload all the work to AI. It is to use AI while still owning the technical judgment.
Which Companies Use AI Coding Interviews?
Tooling, environments, and grading criteria vary significantly by company. Toggle below to check how each company evaluates candidates:
Meta AI Coding Interview
CoderPad SandboxCandidates work in CoderPad with a built-in AI chat panel. The round consists of progressive algorithm challenges (such as building a progressive key-value store under a 90-minute limit) where you are evaluated on problem-solving speed, code quality, and how you steer and verify AI completions in real time.
Practice Problems
Solve progressive multi-part challenges using our built-in workspace environment:
| Status | Title | Difficulty | Target Company | Action |
|---|---|---|---|---|
| ○ | Maze SolverGuided | Medium | Meta | Practice |
| ○ | Max Unique Characters Subset | Hard | Meta | Practice |
| ○ | Card Game Strategy | Medium | Meta | Practice |
| ○ | Friend Recommendation | Easy | Meta | Practice |
| ○ | Compiler Optimization | Hard | Meta | Practice |
| ○ | Card Hand Comparator | Medium | Rippling | Practice |
| ○ | Delivery Cost Dashboard | Easy | Rippling | Practice |
| ○ | Expense Rule Engine | Medium | Rippling | Practice |
| ○ | Service Dependency Impact | Hard | Practice | |
| ○ | Meeting Scheduler | Medium | Practice | |
| ○ | LRU Cache Progressive | Hard | Practice | |
| ○ | Crawler Frontier Queue | Medium | Practice | |
| ○ | Compound Word Finder | Medium | Soon |
How to Prepare for an AI Coding Interview
The core of preparing for an AI coding interview is not getting the AI to write the code for you. It is learning to review, guide, and improve the AI's output like a technical lead. That is the right mental model. You should treat the AI as a tool you supervise, not as the driver of the solution.
Should you use more or less AI?
This is one of the most common questions people ask. AI coding interview is still relatively new and from our conversation with insides, it seems that even the interviewers themselves are not sure about this yet. So the expecations vary across interviewers (and sometimes depends on their preference towards AI).
For example, I know hardcore C++ developer who dislike AI since C++ is very flexible and has so many ways to do things, and AI may not produce the "crafty" code they want. In these cases, they may expect you to use less or minimal AI. On the other hand, if the interviewer is a PHP, Python, or React.js developer, it makes perfect sense to let the AI handle more of the implementation. Just like any interview, it's important to figure out what the interviewers are looking for upfront so you can work towards their expectations.
However, in most cases, you do not want to use too much AI. Letting the AI solve the problem for you entirely (such as prompting the AI to write the whole solution in one go) is a major mistake—as seen in reports of candidates being rejected for using too much AI during Rippling's AI-assisted interviews. What you want is to guide and steer the AI and explain why you are doing it to the interviewer. This is where the core interaction loop shines:
The AI Coding Interview Loop:
Here's a practical "core loop" you can use in an AI-enabled interview.- 1. Think / Use AI to help if needed: Understand the codebase and formulate an approach yourself. Use AI to brainstorm and explain options if needed.
- 2. Explain your thoughts: Talk to the interviewer. Walk them through your plan, explain the algorithm you want to use (e.g. DFS, BFS, sliding window), and why you chose it. Make sure you are aligned on the strategy before writing any code.
- 3. Use AI to implement: Prompt the AI to write specific, modular components (e.g., "Write a queue-based BFS traversal helper for this graph") rather than asking it to solve the entire problem in one shot. Keep your prompts precise and scope-limited.
- 4. Verify AI results: Never trust AI-generated code blindly. Run the local unit tests and verify AI written code.
Build Pattern Recognition
As mentioned earlier, recognizing patterns becomes even more important now that more of the implementation can be done by AI.
- Try AlgoMonster’s AI coding interview practice problems and treat them like real interview loops.
- Practice using the flowchart to recognize patterns
- Use the speedrun feature to strengthen fast pattern recognition and algorithm selection.
Practice Human-AI Collaboration Language
Communication has always been the key in any interview and AI coding interview is no exception. Just like in coding interviews, we should keep interviewer in the loop on what we are doing and why we are doing them and what have done with AI and how we verified its output.
Hiring-Manager-Style Scoring Rubric
If we convert industry feedback and company guidelines into a practical scoring rubric, the signal weights look roughly like:
- Problem Solving (35–40%): Decomposing the problem, selecting correct algorithms/data structures, and understanding invariants.
- Code Understanding & Quality (25–30%): Reading and explaining codebase abstractions, identifying code flow, and writing clean extensions.
- Verification & Debugging (15–20%): Catching AI hallucinations, tracing errors, writing test cases, and verifying edge cases.
- Communication (15–20%): Guiding the AI, discussing trade-offs, explaining your plan, and collaborating with the interviewer.
Problem-solving and code understanding remain the highest-signal areas. AI usage is not evaluated as a standalone skill, but rather through how you verify and communicate around the code it generates.
| Behaviors leading to a STRONG rating | Behaviors leading to a WEAK rating |
|---|---|
| Explains the algorithm before asking AI to implement parts of it. | Prompts AI to solve the entire problem in one go. |
| Reviews AI-generated code line-by-line to verify correctness. | Copy-pastes generated code without explanation. |
| Quickly understands unfamiliar codebases and abstractions. | Does not understand how the generated code fits into the repository. |
| Runs tests frequently and writes custom checks. | Minimal testing or blind reliance on AI output. |
| Catches hallucinations and checks complexity and edge cases. | Treating AI as authoritative rather than as a collaborator. |
| Keeps the interviewer informed of your plan and reasoning. | Poor communication or long silences while waiting for AI. |
Other AI Interview Formats
While hands-on coding is the most common format, companies are expanding AI integration into other evaluation loops. These formats are less about standard algorithm writing and more focused on system architecture or behavioral vetting.
AI System Design & Agentic Loops
Candidates report that some companies are introducing onsite AI System Design or "Agentic" loops. In these rounds, instead of designing standard static service configurations, you design autonomous agent workflows, multi-agent orchestrations, and ReAct loop systems (such as ticket-triage or issue-resolution agents).
The round evaluates your ability to structure LLM prompts, define tool execution boundaries, handle agent loops/timeouts, and manage state context. You can practice building and evaluating these systems in our Agentic Workflows course.
AI-Hosted Behavioral Screening
Some companies (such as Snowflake) conduct fully automated, AI-hosted screening interviews as a non-coding behavioral and work experience filter. You interact entirely with a virtual AI agent over your microphone and webcam, with no human present.