AI Company Interview Process: How ElevenLabs, Decagon, Together AI, Linear, and Vercel Hire

If you prepared for a FAANG loop and then interviewed at an AI company, the first coding round probably surprised you. Nobody asked you to reverse a binary tree. Somebody asked you to build a working feature, then changed the requirements halfway through.

That is not one company's quirk. It is a consistent pattern across a whole generation of employers. The differences from big tech hiring are predictable enough to prepare for directly.

This guide compares six loops. Then it explains the five things these companies do differently and how to prepare for them.

The Loops Side by Side

Every one of these finishes faster than a typical big tech loop. Every one replaces at least one algorithm round with something that looks like the job.

The Five Things AI Companies Do Differently

1. Practical coding replaces algorithm puzzles. Candidates report data manipulation, API implementation, and feature building instead of puzzle problems. The common structure is build-then-extend: you deliver something that works, the interviewer adds a constraint, and your code absorbs it without a rewrite.

2. The design round follows the company's own product. Decagon asks about retrieval systems for support documents. Together AI asks about serving models. ElevenLabs asks about voice product workflows. Generic "design Twitter" preparation transfers only partly.

3. Ownership is tested directly. Almost every loop here spends an hour on one project you personally owned, probing until interviewers find the edge of your real involvement. Side projects with actual users count for more here than at large companies.

4. Behavioral evaluation is spread across rounds. ElevenLabs and Together AI have no dedicated culture screen, so short behavioral questions appear inside technical sessions instead. You are being assessed in every conversation.

5. Timelines are short. Two to five weeks is normal, and decisions often arrive within days of the final round. Tell each recruiter your timeline early, because these companies can usually move to match it.

ElevenLabs: The Product Decomposition Round

ElevenLabs hires against an explicit idea: founders who happen to be looking for a job. The loop runs five stages over three to five weeks.

The coding screen is asynchronous. You get roughly 90 minutes in CoderPad for two medium problems and one medium-hard, scheduled whenever you are sharp. Bank the two mediums efficiently and give the hard one the rest of the hour.

Then comes a project review, followed by a live coding round framed as a customer problem.

The signature round is product decomposition. You are given a use case, often voice-shaped such as an audiobook tool or a dubbing workflow.

You design both halves. First the user experience, meaning the screens and flows. Then the architecture underneath, meaning the APIs, streaming paths, and storage. Finally you sequence it and say what ships first.

That round grades product judgment alongside system design, which is unusual. Preparing only the architecture leaves half the score unearned. Full detail is in the ElevenLabs interview process.

Decagon: Designing AI Agents

Decagon runs five to six stages over three to four weeks. After a recruiter screen comes a 45 minute coding phone screen. The loop that follows has about four rounds: pair coding, system design, a project review, and a behavioral conversation.

The coding rounds are practical. Reported examples include a multi-part API implementation and medium difficulty array problems. Interviewers ask about running time and memory as you go, so state the cost of your code while you write it.

The design round follows Decagon's product, which is AI agents for customer support. Reported topics include ingesting and retrieving support documents using embeddings and vector search.

An embedding is a list of numbers representing the meaning of a text. Vector search finds the stored texts closest to a query. Another reported question involves an AI gateway with audit requirements, meaning one service routes every model request so it can be traced later.

The pair coding round is worth calling out, because the interviewer acts as a teammate rather than a judge. Ask clarifying questions, choose a sensible scope, and finish something that runs. See the Decagon interview process.

Together AI: The Applied ML Systems Round

Together AI usually finishes in two to four weeks. The loop holds a recruiter call, one or two live coding interviews, an applied machine learning systems round, and a final set of team conversations. Some roles add an online assessment first.

The coding rounds are standard in content but demanding in pace. Candidates report two medium problems in about 45 minutes, using arrays, strings, and hash maps. The speed is the real test, so state your plan in a sentence or two, then write.

The applied ML systems round is what separates this loop from a generic one. Reported tasks include implementing an attention primitive, handling streaming generation, and batching requests efficiently.

Attention is the core mathematical operation inside modern language models. Streaming generation means sending output word by word instead of all at once. Batching means grouping requests so the GPU processes them together.

You do not need research-level depth. You do need working code around these ideas and a clear explanation of the trade-offs. One good exercise: write a small program that batches requests to a model API and streams the output back. See the Together AI interview process.

Linear: The Paid Work Trial

Linear runs five stages over four to six weeks. The first four are a recruiter screen, a technical screen of about 60 minutes, a take-home project of a few hours, and a virtual onsite. The technical screen covers practical coding and data modeling.

The fifth stage is what Linear is known for. The work trial lasts two to five days, and Linear pays a daily rate for it while you do real work with the actual team. The company explains the trial openly in its own hiring posts, so it is not a surprise.

The trial changes how to think about the whole loop. Earlier rounds are filters for getting into a working relationship. The trial is where the decision is really made, over several days rather than one hour. See the Linear interview process.

Vercel: Interviews Built to Look Like Real Work

Vercel runs about five rounds over roughly three weeks. Its stated philosophy is that interviews should feel like real work rather than algorithm recitals. Rounds happen in a shared editor, and searching the web during interviews is explicitly encouraged.

Round types come from a menu: coding problems, system design, architecture review, full-stack application building, and project discussion. Most candidates see three or four, depending on the role.

Two practical notes. The initial coding assessment is proctored: camera on for the full 90 minutes, no off-screen breaks. Set your environment up as if it were an exam. And some roles include an asynchronous Byteboard assessment. That one is sometimes waived for candidates with strong public GitHub profiles, which is one more reason to curate your public work. See the Vercel interview process.

Okta: The Security-Shaped Loop

Okta has four to five stages over three to six weeks. The loop holds a recruiter screen, an online coding test, and a hiring manager interview. Then comes a final round of up to three technical interviews plus a behavioral conversation. The online test runs 70 to 90 minutes across about four questions.

Okta sells identity and access management software, meaning tools that control how users log in and what they can reach. That focus shows up in the technical rounds, so expect security questions alongside coding and design.

The online test rewards correct, complete solutions on easy and medium problems rather than clever ones. See the Okta interview process.

How to Prepare for an AI-Native Loop

Practice extending your own code. Solve a small problem, then add a requirement and extend the solution without starting over. Repeat until it feels routine. This single exercise matches the coding format at nearly every company here.

Learn the serving layer. Batching, caching, streaming, GPU utilization, retrieval, and cost per request come up constantly. Grokking the AI System Design Interview covers these as interview questions rather than as theory.

Prepare one project like a design review. Decisions, alternatives you rejected, real numbers, and what broke. A side project with actual users is often your strongest material.

Research the company's product before the design round. The question will look like their business. Half an hour on their documentation changes how that hour goes.

Have your motivation answer ready. These companies ask why you want to work there, early and seriously.

Frequently asked questions

Do AI companies ask LeetCode questions? Less often than large tech companies. Reported loops at ElevenLabs, Decagon, Together AI, and Vercel favor practical implementation, API work, and feature building. Data structure fluency still helps where the pace is fast, but puzzle-pattern recognition rarely decides the outcome.

How long do AI company interview processes take? Two to five weeks is typical, against six or more at many large companies. Together AI reports two to four weeks, Decagon three to four, and ElevenLabs three to five. Linear takes four to six because of the work trial.

What is a work trial, and does it pay? A work trial is a short period of real work with the team, used instead of relying only on interviews. Linear runs one for two to five days and pays a daily rate for it.

Is system design different at AI companies? Yes. The questions follow the company's own product. Expect model serving, retrieval, embeddings, agents, or streaming rather than classic web architecture. Generic system design preparation transfers only partly.

Do these companies have a separate culture interview? Often not. ElevenLabs and Together AI spread behavioral questions through the technical rounds instead. Assume you are assessed on communication and ownership in every conversation.

Are side projects worth more at AI startups? Yes. Several of these loops probe projects you led or created. A side project with real users is direct evidence of the end-to-end ownership they screen for.

If you are preparing for coding rounds across several of these loops, Grokking the Coding Interview organizes practice by pattern.

Related reading

• Anthropic Interview Process: Rounds, Questions, and How to Prepare
• OpenAI System Design Interview Questions
• AI System Design Interview: The Complete Guide
• What Is the Perplexity Interview Process Like?
• What Is the Cursor Interview Process Like?
• What Is the Sierra Interview Process Like?