What coding questions does Scale AI ask?

Mostly practical, multi-part problems rather than LeetCode. The phone screen has been poker hand rules, party time blocks, a task scheduler with deadlines and subtasks, and a four-player card game. Onsite coding is a debugging round in a project-assignment codebase and a backend practical that turns CSV files into JSON and classifies them with an LLM API; some loops add a LeetCode-style grid problem.

What question does Scale AI ask most?

Behavioral and Hiring-Manager Questions is the most frequently reported Scale AI question in this catalog, appearing in 14 candidate reports.

How many Scale AI interview questions does AceOffer cover?

AceOffer covers 17 Scale AI interview questions reconstructed from verified candidate reports covering Jan 2025 – Aug 2026, with reported follow-ups, runnable solutions you can test, and frequency stats. Scale AI is included with any AceOffer subscription.

Scale AI Interview Questions

Reconstructed from 90 verified candidate reports across 17 questions. Jan 2025 – Aug 2026.

This page is a live view of every Scale AI interview question AceOffer has indexed — pulled from real candidate reports, not invented from job descriptions or one founder’s memory. Every question shows how many times it’s been reported and when it was last seen. The catalog gets a refresh pass every month.

candidate reports

distinct questions

round types

Monthly

refresh cadence

Key facts

•17 distinct Scale AI interview questions indexed
•90 candidate reports across the catalog
•Most reported: Behavioral and Hiring-Manager Questions — 14× (last seen August 2026)
•Reports span Jan 2025 – Aug 2026
•Refreshed monthly · last updated August 2026

Browse Scale AI interviews by topic

Coding Interview Questions System Design Interview Questions Behavioral Interview Questions Machine Learning Interview Questions Interview Process & Rounds

The Scale AI loop, from candidate reports

Scale AI's loop is built from practical rounds rather than LeetCode. After the recruiter, the phone screen is one multi-part coding problem in about 60 minutes on HackerRank with your screen shared, often followed by a 30-minute hiring-manager chat. The tests are written for you and candidates report that all of them have to pass. The phone-screen problem has changed over time: poker hand rules in early 2025, a party-schedule problem through early 2026, then a task scheduler with deadlines and subtasks, with a four-player card game reported in mid-2026. Recruiters often send a prep document, and several candidates say it closely matches the real problem. The onsite is usually five rounds over one or two days: a debugging round in an existing codebase with three failing tests, a backend practical that turns CSV files into JSON and then calls an LLM API to classify the data, with AI tools allowed, a system design round, a behavioral or hiring-manager round, and a values round built on the company credo. Some loops add a project presentation or a LeetCode-style coding round, and frontend candidates can take a React practical instead of the backend one. ML roles report an LLM theory round, NumPy implementations of sampling and attention, and debugging an LLM project.

What does Scale AI ask in each interview round?

Scale AI interviews span 6 distinct round types, broken down below. Counts reflect distinct questions per round, not number of times asked. Frequencies on individual question cards show how many candidates reported getting that specific question.

About 60 minutes, one multi-part coding problem on HackerRank with screen sharing and pre-written tests, often paired with a 30-minute hiring-manager chat. Poker hand rules (early 2025), party time blocks (2025 to early 2026), a task scheduler with subtasks (2026) and a four-player card game (mid-2026).

4 questions

Most-reported: Party Time Blocks and Dead-Zone Hours (12 reports)

A debugging round in an existing project-assignment codebase with three failing tests; a backend practical that reads CSV files into JSON behind a local endpoint and then classifies records with an LLM API, with follow-ups on validation and scaling; on some loops a LeetCode-style grid problem; and for frontend candidates a React practical.

4 questions

Most-reported: Backend Practical: CSV to JSON, Then LLM Classification (8 reports)

For ML roles: an LLM theory round from regularization and tokenization through fine-tuning and RL methods, NumPy implementations of top-p sampling and attention in Colab, debugging an LLM project after writing a data parser, and take-homes built on jailbreaking GPT-2: a 2025 intern take-home with a prompt-engineering question and a jailbreak algorithm to implement, and a 6-hour Colab OA that implements the attack from a named paper.

4 questions

Most-reported: LLM Theory Round: Transformers, Sampling, Fine-Tuning and RL (5 reports)

A behavioral round, a hiring-manager conversation about your projects, and a 30-minute credo round on the company values; some hiring-manager rounds are introduced as a chance to ask questions but still ask behavioral ones. One loop ended with a separate 'customer engagement' round that asked for the candidate's current manager's name and what score that manager would give them.

2 questions

Most-reported: Behavioral and Hiring-Manager Questions (14 reports)

Pipelines around black-box model services: an async pipeline that fans work out to an LLM and has to survive timeouts, rate limits and unreliable output, and an embedding and classification API that serves both low-latency and high-throughput traffic with cost in mind.

2 questions

Most-reported: Design a Pipeline Around a Black-Box Model Service (5 reports)

A project presentation of 30 to 45 minutes on your own work or a paper; interviewers may be generalists, so candidates advise making it metrics-driven and easy to follow outside your field.

1 question

Most-reported: Project Presentation Round (4 reports)

Which Scale AI interview questions come up most?

These are the Scale AIquestions reported most across the loops we’ve indexed, sorted by candidate-report frequency.

Question	Round	Reported	Last seen
Behavioral and Hiring-Manager Questions	Behavioral	14×	August 2026
Party Time Blocks and Dead-Zone Hours	Phone Screen	12×	March 2026
Task Scheduler with Deadlines and Subtasks	Phone Screen	10×	August 2026
Backend Practical: CSV to JSON, Then LLM Classification	Coding	8×	August 2026
Debug the Project-Assignment Codebase	Coding	6×	May 2026
Poker Hand Rules with Wildcard Jokers	Phone Screen	6×	May 2025
LLM Theory Round: Transformers, Sampling, Fine-Tuning and RL	MLE	5×	April 2026
Design a Pipeline Around a Black-Box Model Service	System Design	5×	April 2026
Project Presentation Round	Tech Deep Dive	4×	August 2026
Credo (Company Values) Round	Behavioral	4×	April 2026

The full index is below, or browse the Scale AI catalog →

Every Scale AIinterview question we’ve indexed

All 17, grouped by round and sorted by how often candidates reported them. Each links to the question, its reported follow-up count, and when it was last seen.

Phone Screen (4)

Party Time Blocks and Dead-Zone Hours — reported 12×, last seen March 2026
Task Scheduler with Deadlines and Subtasks — reported 10×, last seen August 2026
Poker Hand Rules with Wildcard Jokers — reported 6×, last seen May 2025
Four-Player Trick-Taking Card Game — reported 2×, last seen August 2026

Coding (4)

Backend Practical: CSV to JSON, Then LLM Classification — reported 8×, last seen August 2026
Debug the Project-Assignment Codebase — reported 6×, last seen May 2026
Frontend Practical (React): Two Reported Versions — reported 2×, last seen August 2025
Neuron Grid State Update (LC 289 Variant) — reported 2×, last seen May 2026

MLE (4)

LLM Theory Round: Transformers, Sampling, Fine-Tuning and RL — reported 5×, last seen April 2026
ML Debugging: Parse Conversation Data, Then Fix an LLM Project — reported 3×, last seen March 2026
ML Take-Home: Reproduce a GCG Jailbreak on GPT-2 — reported 2×, last seen December 2025
ML Coding: Top-p Sampling and Multi-Head Attention in NumPy — reported 2×, last seen March 2026

Behavioral (2)

Behavioral and Hiring-Manager Questions — reported 14×, last seen August 2026
Credo (Company Values) Round — reported 4×, last seen April 2026

System Design (2)

Design a Pipeline Around a Black-Box Model Service — reported 5×, last seen April 2026
Design an Embedding and Classification API — reported 3×, last seen August 2026

Tech Deep Dive (1)

Project Presentation Round — reported 4×, last seen August 2026

What passing candidates do

•Reading the recruiter's prep material closely. Several candidates say the prep document or portal page describes the phone-screen problem almost exactly, and one recruiter sent a detailed description when scheduling.
•Getting every provided test to pass. Phone-screen candidates report that all tests must pass to count, and in the debugging round one candidate was told every bug had to be found.
•Talking through the debugging. Candidates describe printing intermediate results to trace the code, and say interviewers care about how you communicate while you search.
•Using AI tools in the backend practical, but writing your own prompt. Several reports say AI assistants are allowed, though one says they were not. One candidate finished in under 20 minutes with one, but was stopped from pasting a screenshot of the problem straight in and asked to write at least the prompt themselves.
•Choosing Python. Recruiters recommend it for the phone screen, and candidates report that Java makes the multi-part problems hard to finish in time.

Where candidates lose points

•Losing time to long problem statements. Several phone-screen candidates spent much of the hour working out what the problem was asking, then ran out of time for the last part or for running tests.
•Making the tests pass without fixing the logic. One candidate passed every test by reverse-engineering the expected output, left a bug in place, and was rejected.
•Running out of time before testing. In the backend practical and the phone screen, candidates describe finishing the code at the last minute with no time left to run it.
•Relying on the technical rounds alone. One candidate whose technical rounds were all positive was rejected on the hiring-manager round, and another was turned down after an HR screen that asked detailed project questions.
•Preparing the backend practical so thoroughly that the interviewer cannot see your process. One candidate was told they had prepared too much, so the interviewer could not see how they actually use a model.

Get the full Scale AI catalog

Every question. Every candidate-reported follow-up. The mistakes that sink people, and what passers do instead. Monthly refresh.

See pricing Browse free first

More company interview guides

Anthropic Interview Questions→OpenAI Interview Questions→Waymo Interview QuestionsFree→xAI Interview Questions→Perplexity Interview Questions→Mercor Interview QuestionsFree→Palantir Interview Questions→Harvey AI Interview QuestionsFree→Roblox Interview Questions→Zip Interview Questions→Robinhood Interview Questions→Databricks Interview Questions→Snowflake Interview Questions→Cursor Interview QuestionsFree→Citadel Interview Questions→Stripe Interview Questions→

Prefer to study first? Free reading guides & references — including the AI Training Papers reading guide.

Know someone interviewing at Scale AI?

Send them this guide: 17 questions from 90 candidate reports, with the rounds, the follow-ups and what passers do.