DoorDash Replaced the Coding Round With an AI Session. The Other Shoe Hasn't Dropped Yet.

I've watched the Big Tech interview bar shift five or six times in the last decade. Most shifts are cosmetic: a new values framework, a different leveling chart, an extra round added to the loop. They matter for candidates in the moment, but they don't change what the interview is measuring. A system design round graded on "scope and tradeoffs" is still the same round whether you call it "L5 system design" or "staff architecture deep-dive."

What DoorDash did in late 2025 is not a cosmetic shift. It is a change in what the interview measures. And if you're reading this thinking "so the AI companies finally caught up", I want to tell you something uncomfortable: the AI-native interview is not about AI. It's about what you can't hide anymore.

Let me walk you through exactly what happened, what the new round looks like, and why the rest of the industry has not followed DoorDash yet — and what that means for the person who's about to interview.

What DoorDash actually did

In 2025 DoorDash published a pair of posts on its engineering careers blog about rebuilding its engineering interviews around AI-assisted workflows. The anchor post — Why DoorDash is rebuilding its engineering interviews around AI — lays out the rationale and the rubric. The DoorDash careers blog index has the companion posts on the candidate guide and internal pilot results.

The policy, stripped of corporate framing, is this:

1. The traditional live-coding round is gone for most DoorDash engineering roles. Not deprecated. Not "we still do it but it's optional." Gone.
2. In its place: a 60-minute AI-assisted engineering working session. Candidates bring their own IDE — Cursor, Claude Code, Codex, VS Code with Copilot, all acceptable on free-tier. No corporate licensing requirement. No "preferred tool" list.
3. The session starts with a realistic DoorDash-flavored starter project. Candidate reports describe starter projects that mirror real DoorDash work: implementing an order dispatch system, building a smart menu composer, automating a support-request resolution system. You clone the starter, you read enough to understand it, and then you extend it.
4. All AI features are permitted and expected. Chat with the model. Inline suggestions. Agent/autopilot mode. Running commands through the tool. Planning with the AI before writing code. The candidate guide DoorDash sends before the interview explicitly says: use every feature your tool has. Candidates who avoid AI because they think it's cheating are penalized, not rewarded.
5. "Prompt engineering ability" is graded as an explicit interview dimension. This is the line that made me sit up straight when I first saw the rubric. I've been watching interviewer rubrics for a long time and I have never seen "prompt engineering" appear as a scored dimension on a first-round coding interview. It is now.

The post-script matters as much as the policy itself. DoorDash's engineering team reported that in the early pilot, many candidates arrived without a configured IDE or believing that using AI to its fullest potential was somehow cheating. So they now send a detailed pre-interview guide that tells candidates: install Cursor, log in to Claude Code, enable inline suggestions, practice autopilot mode on a small project before the interview. They are teaching candidates how to interview. That's how serious DoorDash is about this.

The six-dimensional rubric

The DoorDash AI-assisted round grades six dimensions. Four of them are things interviewers always cared about; two are genuinely new. Here's the shape of the rubric and what each dimension is actually testing:

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Phrased from the rubric itself:

1. Orientation speed. How fast can you read an unfamiliar codebase, identify the 2–3 load-bearing files, and figure out what the starter project does? In the pre-AI interview, this was hidden — candidates would ask the interviewer questions. In the AI interview, you can ask the AI. The clock is running. Slow orientation is the first thing that flags a weak candidate.
2. Use of AI and verification of outputs. Can you prompt the model to generate something useful, and can you tell when its output is wrong? This is the new version of "does the candidate trust their tools appropriately." A candidate who takes Copilot's suggestions at face value without reading them is graded the same as a candidate who types everything from memory and gets it wrong. The AI is a force multiplier — including on your mistakes.
3. Debugging. When your code doesn't work, can you figure out why? AI or no AI — ultimately someone has to look at the stack trace and connect it to the broken line. Candidates who loop "maybe the AI will fix it" without forming a hypothesis get penalized hard.
4. Scope management. Realistic DoorDash starter projects are always bigger than 60 minutes. You cannot build all of it. Can you identify what actually needs to ship in this session to demonstrate the skill? Scope management is the senior signal here — junior candidates try to build everything and finish nothing.
5. Tradeoff communication. Narrating out loud, explaining why you're taking Path A vs Path B, what the failure modes are, what you'd do differently if you had more time. This is the round where interviewers stop grading whether your code compiles and start grading whether they'd want to work with you.
6. Prompt engineering ability. Explicitly called out in the DoorDash guidance. Can you write a prompt that gets the AI to do the thing you want in one shot, or do you take three attempts to clarify, then another three to debug what it wrote? Sharp prompts are a senior signal. Vague prompts that produce near-miss code are a junior signal.

Here's the hard truth embedded in that rubric: four of the six dimensions are things interviewers always cared about. Orientation speed, debugging, scope, and tradeoff communication have been scored on every decent coding round I've ever graded. The AI-native round doesn't replace those — it sharpens them, because the AI strips away the time candidates used to spend typing boilerplate and forces the higher-order signals into the foreground.

The two genuinely new dimensions are use of AI and verification and prompt engineering. Both are learnable. Neither is magic.

The rest of the industry is NOT here yet

If you read the DoorDash post and thought "right, so this is where we're all going," hold that thought for a second. As of April 2026, DoorDash is alone in this position among the companies the StrongYes content directorate tracks. Everyone else is somewhere else on the spectrum:

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The left end of that spectrum is AI-prohibited in live coding. Apple, Microsoft, Stripe, Amazon, Google all still take this position officially. Stripe's phrasing in their recruiting guidance is clean: during live interviews, candidates are asked to rely on their own skills, knowledge, and judgment, and to avoid AI or transcription tools unless explicitly permitted. Every one of those companies is on record telling candidates to interview without AI tooling. This is not going to last forever, but it's the current state.

One step right: Meta. Meta launched an AI-assisted coding interview pilot in October 2025 and has since expanded it. As of early 2026, candidates at E6 or below get an AI-assisted round in addition to a traditional coding round. Candidates at E7+ and M1 get only an AI-assisted coding round — no traditional round at all. The format uses a CoderPad environment with a built-in AI assistant offering models like Claude Sonnet, GPT-5, and Gemini 2.5 Pro. Interviewing.io's breakdown has real prompts and examples from inside the round. This is the additive-to-native position: Meta is moving faster than the AI-prohibited companies but hasn't fully replaced the traditional coding round across all levels yet.

Two steps right: DoorDash. They are alone in the native rebuild position. Nobody else has committed to replacing the traditional coding round with an AI session. Nobody.

Three steps right: broad adoption, which doesn't exist yet but which every observer I've talked to thinks is coming. Shopify and Canva are culturally in the AI-friendly camp but haven't published a formal rebuild. Stripe and Meta are likely candidates for a 2026–2027 follow-on. The timing is not public; the direction is inevitable. CoderPad's State of Tech Hiring 2025 report tracks AI adoption climbing sharply on the recruiter side. CoderPad's 2026 follow-up confirms the trend: 82% of developers now find GenAI at least somewhat useful (up from 76% in 2025), and AI/LLM application skills are a top-3 focus area for developers surveyed. The candidate side is always a year or two behind the recruiter side in practice.

If you're interviewing at a company in the AI-prohibited bucket, nothing I'm about to say about prep changes anything for you in the next six months. Practice the way you've been practicing. But if you're interviewing at DoorDash, if you're in Meta's E6- pilot window, or if you're playing a longer game and interviewing anywhere in 2027, you need to start prepping for the AI-native round now. The muscle takes weeks to build, not hours.

Why DoorDash (and not someone else)

Two questions I had when I first looked at this: why DoorDash? and why this specific form of the rebuild?

The why DoorDash answer is hiding in the DoorDash engineering culture, and it's not flattering to the rest of the industry. DoorDash is a high-growth company whose production system is Kotlin, Python, and React, whose on-call load is heavy because they run a real-time three-sided marketplace, and whose engineers write code that interacts with complex legacy systems every day. The kind of engineer DoorDash needs to hire is not someone who can optimize a two-pointer algorithm in 15 minutes. It's someone who can drop into a foreign codebase at 2 a.m., figure out what's happening, and ship a hotfix. The AI-native round is a much better test for that skill than a blank-editor LeetCode problem is.

Phrased bluntly: the traditional coding round tests whether you memorized patterns. The AI-native round tests whether you can build in an unfamiliar system under pressure. DoorDash's engineering is an unfamiliar system. They are testing for the job.

The why this form answer is about signal-to-noise. DoorDash tried a few variants of this round internally before shipping it. The candidates- bring-your-own-IDE decision isn't an ergonomics call — it's a signal call. If you hand every candidate the same IDE with the same configured AI tools, you're testing how fast they can figure out the setup you gave them. If you let them bring their own, you're testing whether they already work this way. "Show me how you build things at home" is a much higher-signal question than "show me how you build things on our rental laptop."

The cost of the bring-your-own-IDE decision is that it disadvantages candidates who don't already have a professional AI-assisted workflow. That's a real cost. DoorDash's counter-move was to publish the candidate guide telling people exactly what to install and practice. It's an admission that this is a new skill the industry has to learn — and that DoorDash would rather teach it than gatekeep around it.

How to actually prep for it

If you have a DoorDash interview scheduled (or you're expecting one in 2026), here's the prep plan I'd give a senior engineer I was coaching. InterviewQuery's DoorDash guide and Prepfully's exhaustive breakdown cover the full loop structure; what follows is the AI-specific muscle you need to build. It's cheap, it's boring, and it's what works.

Week 1 — Tool proficiency. Install Cursor, Claude Code, and Copilot. Pick one as your daily driver (I recommend Claude Code for agent workflows, Cursor for inline suggestions — try both, pick the one that matches your habits). Use it for every piece of coding work you do this week. Do not use the tool in "code completion" mode only. Use the chat, the inline edit, the planning mode, the autopilot. Learn the keybindings. If at the end of Week 1 you still have to think about which keystroke triggers the AI, you have not used the tool enough.

Week 2 — Orientation drills. Take a random open-source repository from GitHub — something you've never seen, with 50–500 files, ideally in a language you don't work in daily. Set a 10-minute timer. Your goal: read enough of it to answer three questions: what does this project do?, what are the 2–3 load-bearing files?, and if I had to add a new feature, where would I put it? Use the AI actively during this — ask it to summarize files, ask it to explain functions, ask it to point you at entry points. Do this with five different repositories. By the end of Week 2 you should be able to orient in an unfamiliar codebase in under 10 minutes.

Week 3 — Ship-under-time drills. Find a starter project on GitHub that's intentionally incomplete — something with a // TODO: implement this marker. Set a 60-minute timer. Your goal: ship a working extension to the project before the timer hits zero. Narrate out loud as you work (literally — record yourself on your laptop's voice memo app). At 60 minutes, stop and listen to the recording. Where did you stall? Where did you take an AI suggestion without verifying it? Where did you write code from memory that the AI would have written for you in five seconds? Those are the coaching points.

Week 4 — Tradeoff articulation. Take the projects you worked on in Week 3 and write a one-page post-mortem for each. What were the design tradeoffs? What would you do differently if you had two hours instead of one? What's the first thing that would break in production? Tradeoff articulation is the thing candidates most often flunk under pressure — the muscle that lets you answer "why did you choose that approach?" without pausing. Writing it down five times in a quiet room is the fastest way to build it.

At the end of that month, you should be able to sit in a DoorDash-style AI-assisted working session and treat the AI as an extension of your fingers rather than a separate agent you're arguing with. That's the bar.

The part I want you to take away

There's a real temptation, looking at what DoorDash did, to feel like the AI-native interview is a gimmick or a cheap way for DoorDash to ride a trend. I want to push back on that read. I've been grading coding interviews for a long time and I have rarely seen a round structure this well-aligned with the actual job. The traditional coding round is a dinosaur. It was designed to filter candidates at a time when writing syntactically correct code was the bottleneck on engineering velocity. That's not the bottleneck anymore. The bottleneck is reading unfamiliar systems, forming good hypotheses, communicating tradeoffs, and shipping under pressure — and the AI-native round tests every one of those directly.

The uncomfortable corollary is that if you've built your career on being the person who can type blank-editor LeetCode solutions faster than anyone else, the next three years are going to be humbling. That skill is still worth something. It is not worth as much as it used to be.

What's worth more: being the person who can walk into a codebase you've never seen, use AI tooling like a craftsman rather than a crutch, ship something that works, and explain what you did in plain English. That's what DoorDash is testing for. It's what every real engineering job is actually about. And it's a learnable skill — you just have to start now.

DoorDash fired the first shot. The rest of the industry is going to follow. The only question is whether you'll be ready when they do.

Citations and Further Reading

• DoorDash — Why DoorDash is rebuilding its engineering interviews around AI. careersatdoordash.com/blog/doordash-is-rebuilding-its-engineering-interviews-around-ai. Primary source. The anchor post laying out the AI-assisted round policy, rubric, and rationale. The blog index has companion posts on the candidate guide and pilot results.
• Exponent — DoorDash Software Engineer Interview Guide. tryexponent.com/guides/doordash-software-engineer-interview. External candidate-facing guide with the full loop structure, including the notorious "WeDash" lunch-delivery trap and the values round.
• Interviewing.io — How to use AI in Meta's AI-assisted coding interview. interviewing.io/blog/how-to-use-ai-in-meta-s-ai-assisted-coding-interview. Real prompts and examples from Meta's AI round. Essential reading for understanding where Meta sits on the spectrum relative to DoorDash.
• InterviewQuery — DoorDash Software Engineer Interview Guide (2026). interviewquery.com/interview-guides/doordash-software-engineer. Updated 2026 guide covering debugging, frontend, and the full process including AI round changes.
• CoderPad — The State of Tech Hiring 2025 & 2026. 2025 report | 2026 report. Hard numbers on AI adoption in hiring. 82% of devs find GenAI useful in 2026, up from 76% in 2025.
• Stripe — careers and interview guidance. stripe.com/jobs. Stripe's "rely on your own skills during live interviews" policy. Consistent across candidate-facing documents as of April 2026.