Working in AILesson 52 of 85

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Working in AI

Lesson 52 of 85. Sign in to keep your place.

Module 6 · The interview, from first screen to signed offer

The take-home, and how not to lose on it

9 min read

What the grader is actually scoring

Almost never “did they finish everything”. Usually, in this order:

  1. Does it run? From a clean checkout, following the README, on their machine. A surprising share of submissions fail here, and it is an immediate no.
  2. Can I read the README in two minutes and know what you did?
  3. Did you notice the mess? Every well-made take-home dataset has a deliberate trap.
  4. Would I want to review this person’s code every week? Names, structure, comments where the reason is not obvious.
  5. Did you say what you would do next?

Note that four of the five are communication.

Time-boxing, and saying so

If the brief says four hours, spend four to five, and write at the top of the README what you spent and what you cut.

Text
Time spent: about 4.5 hours. With another day I would have
added tests for the currency parsing and looked at why 6%
of rows have no customer_id.

Spending twenty hours and not saying so is dishonest, and it also loses: the grader is comparing you against a four-hour bar, and a submission that is polished far beyond it reads either as a misjudged sense of scope or as a candidate who could not stop. Judgement about scope is part of what is being tested.

The trap is the test

Every good take-home dataset contains something planted: duplicate rows, mixed timezones, a currency column in three formats, an ID column with leading zeros, negative quantities that mean returns, or a target that leaks.

You do not have to fix all of them. You do have to name them. A README section headed What I found in the data with four bullets scores better than a silent perfect model, because it shows you looked.

Leakage is the classic and worth understanding properly. You are asked to predict which customers will request a refund. The dataset contains refund_processed_at. Any model using it scores near-perfect and is worth nothing, because at prediction time that column is empty: it only exists after the event you are predicting. The mechanism is that the feature is downstream of the target rather than upstream of it. Interviewers plant this deliberately, and catching it is worth more than any modelling choice you make.

The README that scores

Same skeleton as your project pages:

  • How to run it. One command if possible.
  • What I did, and why: the two decisions that mattered.
  • What I found in the data.
  • How I checked it: the number, however small.
  • What I did not do, and what I would do next.
  • Time spent.

The red lines

Some take-homes are not assessments.

  • A brief that is a real business deliverable: “here is our actual dataset, produce the report we need this month”.
  • More than about eight hours of unpaid work.
  • “Build a prototype of our product” before any technical conversation.
  • A take-home given before anyone at the company has spoken to you at all.

Decline these politely and offer an alternative: ”I would rather not spend twenty hours before we have talked. I am happy to do a live 90-minute session, or to walk you through a project I have already built.” Some companies will accept. The ones that will not have told you how they will treat your time later, which is worth knowing for free.

Asking for a longer turnaround is different and almost always granted. If you have a full-time job, ask for a week rather than 48 hours. Refusal to accommodate that is itself information.

Deploying it for free

If the brief wants something running, you do not need to pay for hosting:

  • Hugging Face Spaces: free CPU tier, good for a demo with an interface.
  • Fly.io and Render: free or near-free small instances.
  • GitHub Pages: for anything static.
  • Streamlit or Gradio: a working interface in forty lines, both free and open source.

A deployed link takes the reviewer five seconds to check and a repository takes five minutes. That difference decides some processes.

The limitation

Take-homes systematically disadvantage people with a full-time job, caring responsibilities, or unreliable electricity and internet, because they convert the assessment into a test of available spare time. Nothing you do fixes that for the industry. What you can do is negotiate the deadline, state your time budget clearly, and treat a company that will not move on either as having answered a question about themselves.

Every lesson here is open. Your progress is recorded once you pass the AI at Work exam.

Check your understanding

A take-home asks you to predict which orders will be returned. Your model scores 0.98 AUC on the first attempt, far above anything you expected. What should you do first?

Pick the one you would defend. Your answer stays private.

Next lesson · 8 minThe live exercise, where silence is scored

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How to cite: Addaly (2026) ‘The take-home, and how not to lose on it’, in Working in AI. Edited by M. Mun. Addaly. Available at: https://addaly.com/learn/ai-careers/the-take-home (Accessed: 13 October 2026).