Scale Ai · Software Engineer
Updated · 2026-11-03

Scale Ai Software Engineer
Interview Guide

THE 60-SECOND BRIEF

At Scale Ai, a Software Engineer is at the absolute center of the artificial intelligence revolution. The company's core mission is to build the data infrastructure that powers the world's most advanced large language models (LLMs) and generative AI applications. As an engineer here, you will not just be writing standard application code; you will be designing, building, and scaling the foundational pipelines that enable reinforcement learning through human feedback (RLHF), model safety, evaluation, and fine-tuning.

Getting the code to run is the floor. What usually separates answers is the case checked without prompting: empty input, a single element, duplicate keys, or a value that overflows the integer type you chose.

Scale Ai candidates report 8 rounds · ≈ 4-6 weeks. The stages below are what candidates describe, not a published process.

Bound blast radius with per-tenant concurrency limitsKeep money in integer minor unitsMake every write idempotent under client retries

38 min read

Practice 30+ Scale Ai questions

See the practice prompts

30Company bank questions ↗Snapshot · Nov 9, 2026 PT4Candidate experiences ↗Read their reports
13Practice promptsAcross five skill areas
3With worked solutionsIncluded in the practice prompts

At Scale Ai, a Software Engineer is at the absolute center of the artificial intelligence revolution. The company's core mission is to build the data infrastructure that powers the world's most advanced large language models (LLMs) and generative AI applications. As an engineer here, you will not just be writing standard application code; you will be designing, building, and scaling the foundational pipelines that enable reinforcement learning through human feedback (RLHF), model safety, evaluation, and fine-tuning.

The impact of this role is massive. Scale Ai acts as the primary data engine for industry giants like Meta, Cisco, and various U.S. government agencies, including the Army and Air Force. Your work directly influences how humanity interacts with AI, ensuring that models are safe, aligned, and highly capable. Whether you are working on the Generative AI Data Engine, SGP, or Donovan, you will be solving complex distributed systems problems, handling billions of data points, and optimizing high-throughput workflows.

This position requires a unique blend of systems-level thinking, algorithmic efficiency, and product intuition. operates at hyper-growth speed, meaning engineers are expected to take complete ownership of their projects, move fast, and deploy robust software that can handle immense scale. It is an intense, intellectually demanding environment, but one that offers unparalleled exposure to the cutting edge of AI technology.

For a single case taken from pipeline design to a ship-or-hold call, with reviewer disagreement, quality slices and review cost in the arithmetic, read PracHub's Scale AI catalog-tagging case.

01

Recruiter Screen

reported

The person on this call usually cannot evaluate your code and does not need to. They write a short paragraph, and that paragraph is what a hiring manager skims when deciding who to put on your loop. So the test is not whether your work was hard, it is whether a non-engineer can repeat it correctly. Name systems by what they did rather than by their internal codename, give each project a shape (what was breaking, what you changed, what happened after), and keep the whole walkthrough near ninety seconds. Depth that cannot survive a paraphrase reads as vagueness.

What to demonstrate

  • Whether a non-engineer can restate your projects without distorting them, since their paraphrase is what travels to the hiring manager, not your sentences
  • Whether each project has a shape rather than a stack list: the failure or constraint, the change you made, the result and how it was measured
  • Whether you can say what was yours inside a team project without either inflating it or disappearing into the plural

How to prepare

  • Rewrite each headline project as two sentences with no internal system names and no acronyms outside your company, then say them to someone outside engineering and have them repeat them back. Fix whatever came back wrong
  • Attach one measured number to each project: the baseline, the change, and the window it was measured over. Where nothing was ever measured, say that plainly rather than reaching for a plausible percentage
  • Time the background walkthrough against a clock. If it runs past two minutes, compress the earliest role to a single clause and spend the recovered time on the most recent one
PracHub interview research ↗
02

Technical Screen

reported

What this round decides is narrow: whether you can produce code that runs and is correct on inputs nobody showed you. An elegant solution that does not compile scores below a plain one that does, so write a correct brute force first, say out loud that you know its cost, and improve it with the working version still on screen. What separates strong answers is who finds the broken case. Trace your own code against an empty input, a single element, and duplicate keys before you say you are finished, because being told is far more expensive than noticing.

What to demonstrate

  • Whether degenerate inputs get checked without being asked for: an empty collection, one element, every element equal, and the extreme value the input type allows
  • Whether the complexity you state matches the code you actually wrote, including a sort or a copy sitting inside a loop
  • Whether the finished answer is verified against the worked examples before you call it done, rather than assumed correct because the code reads correctly

How to prepare

  • Take five problems you have already solved and, without running anything, write down what each returns for empty input, a single element, and all-duplicates. Then run them and count how many you predicted wrong.
  • Drill the brute force as its own skill: on ten problems, write only the obviously-correct slow version and time how long it takes to get it passing. If that is more than a few minutes, that is what to practise, not the optimal version.
  • Add a fixed last step before you submit anything, reading only the loop bounds and the initial value of each accumulator, which is where most off-by-one errors live
PracHub interview research ↗
03

Virtual Onsite

reported

Where the day includes a partner from product, design or data, that conversation is weighted like the technical ones and prepared for least. They are deciding one thing: whether having you in the room makes their decisions cheaper. That means options with costs attached, not implementation detail and not "it depends". An estimate someone can plan against — a range, the assumption that would push it to the high end, and what you would drop to hit the low one — is worth more than a confident single number, which everyone present already knows is wrong.

What to demonstrate

  • Whether an estimate comes as a range with the assumption most likely to break it, and states what a specific scope cut would actually buy
  • Whether a technical constraint is handed over as a choice with consequences on their side, rather than as a verdict they have no standing to argue with
  • Whether you establish what decision is on the table before proposing anything
  • Whether risk is raised while it can still change the plan, with the trigger that would confirm it, instead of reported afterwards as a slip

How to prepare

  • Take a project that shipped late and write the two-sentence warning you could have given three weeks earlier, naming what you would have needed decided at that point
  • Rehearse one estimate out loud until it arrives in three parts: the range, the single assumption that would blow it, and the smallest thing you would cut to protect the date
  • Rewrite an objection you have actually made — the "we can't do that" version — as two options with their costs, so the choice ends up with the person who owns it
PracHub interview research ↗
04

Algorithmic Coding

reported

The same problem is scored by two different mechanisms depending on the format, and preparing for one does not cover the other. With a person watching, partial progress is visible and a hint is a correction you can absorb; silence is the expensive failure, because nobody can read a half-written function. With an automated grader there is no partial credit for what you were about to do, nobody to ask, and the worked examples in the prompt are the entire specification. Read them as a contract, down to whether an empty result should be an empty list or no output at all.

What to demonstrate

  • In a live session, whether your commentary tracks what your hands are doing, and whether a hint redirects you or gets defended against
  • In an automated one, whether you cover the cases the examples do not show, since the hidden cases are where the score moves
  • Whether you manage the clock on purpose: abandoning an approach that is not converging while there is still time to write something simpler that finishes

How to prepare

  • Have someone hand you a problem and feed you one deliberately wrong hint. Practise testing it against a concrete case instead of accepting or rejecting it on authority.
  • Do one timed run a week in a plain browser editor with autocomplete, linting and your own snippets switched off, which is closer to what these environments give you
  • For the automated format, write the harness before the solution: a main that feeds the worked examples plus an empty and a single-element case and prints expected against actual, so a wrong submission is caught by you first
PracHub interview research ↗
05

Debugging

reported

When a round has no standard shape, it is often there because something is still open: an area no earlier conversation reached, a round where the signal came out mixed, or a decision someone is not ready to make alone. Work out which by going back over what each earlier round actually covered rather than how it felt, and arrive able to give evidence on that point without being asked twice. Weak answers replay the loop's earlier material at the same depth. Strong ones go a level deeper and stay consistent with what you already said.

What to demonstrate

  • Whether your account of a project matches the one you gave earlier in the loop, since what you said before may be available to whoever runs this round
  • Whether you can go a level deeper on something already covered, reaching the decision and its alternatives rather than repeating the summary
  • Whether you state your own uncertainty accurately, including parts of a system you did not build and decisions you inherited, instead of claiming even ownership across all of it
  • Whether you can answer a question you handled poorly earlier by naming what you missed, rather than delivering a polished second version as if the first had not happened

How to prepare

  • Reconstruct the loop on one page: for each round, the questions you were asked and the answer you actually gave, not the better one you thought of afterwards. The gaps on that page are your best available guess at why this round exists.
  • Take the two claims you made earlier that carry the most weight and assemble the backing for each: the measurement, the date, what broke, the decision you would make differently now.
  • Write down the three facts about your work that must not drift between tellings, such as team size, timeline and your own role, and check your stories against that list rather than trusting recall under pressure
PracHub interview research ↗
06

Backend API Design

reported

An unlabelled round is first an information problem, and the cheapest information is free. Whoever schedules it can usually tell you how long it runs, who will be in the room and what they work on, whether you will be writing code and in what environment, and whether anything is being sent beforehand. Ask in writing so the answer is on record, then prepare for the two or three formats those answers still leave open instead of betting on one. What separates a strong candidate is not guessing right; it is having an opening that works whichever one it turns out to be.

What to demonstrate

  • Whether you can start work from an ambiguous brief, since tolerating a vague scope without stalling is the same thing the job asks for
  • Whether the questions you asked beforehand were ones that change your preparation, such as duration, medium and who is joining, rather than ones whose answers you could not have acted on
  • Whether you adapt when the round turns out to be something other than what you were told, instead of spending the first ten minutes visibly recalibrating

How to prepare

  • Send one short scheduling message asking four things: how long, who is joining and what they work on, whether you will be writing code and where, and whether to prepare anything in advance. Treat a vague reply as real information, since it means the round is loosely structured and you will be shaping it yourself.
  • Write one opening that works in any of the formats still open: restate in your own words what you have been asked to do, then ask which of two directions is more useful to them. Say it aloud until it stops sounding recited.
  • Set up for the two most likely formats before the call starts, with a blank editor in the language you would choose and a shared document you can type into, so a format surprise costs you nothing in the first minutes
PracHub interview research ↗

4 candidate reports. Individual accounts describe a particular role and hiring cycle.

Software Engineer

Scale AI Software Engineer Interview Experience — Question-Bank Loop, Rude Hiring Manager

Technical Screen → Onsite

Phone screen: the classic question from the interview reports, Task Processor: Dependencies and Deadlines. VO: Project deep dive: normal. The interviewer only asked some follow-ups on project details and didn't ask many BQ questions. SD: the classic embedding + classification API question from the interview reports. Practical Coding: the CSV-to-JSON question that's in the interview reports. Credo…

Read full experience
Software Engineer

Scale AI Software Engineer Interview Experience — Impatient Interviewer, Essay-Length Question

Technical ScreenOutcome: rejected

I'd looked at this company before and thought about applying, but I never really believed I'd pass an interview here, so I went in with a "just try it" mindset. The whole interview was really contradictory. You'd think a technical interview would be short and to the point, but the interviewer was very impatient the whole way through. He gave only a very brief, shallow rundown of himself and what…

Read full experience
Software Engineer

Scale AI Software Engineer Interview Experience — Debug a Real Codebase, Then an LLM API Round

Technical Screen → Onsite

Phone Screen It was the "party" question that's already been posted on the forum, pretty easy. Debug This round felt a bit harder. The question was the "assign project" one that's already on the forum, and the main issue was that time was tight. It's a new codebase with several files, and you need to go back and forth through it a lot (would've been a lot easier if I could've just used AI for thi…

Read full experience

PracHub editorial advice for the preparation topics above.

01

Checking a quota with a select and then writing

Under read-committed isolation, two concurrent transactions both observe a count below the limit and both insert, so the limit is exceeded by exactly the concurrency. Repeatable read does not rescue it either: it provides a stable snapshot, and this is write skew, which snapshot isolation permits by design. The options are serialisable isolation, which detects the conflict and aborts one transaction with a serialisation failure and therefore obliges the caller to retry; a single statement with the predicate inside the write; or a constraint that makes the surplus insert fail outright. The reason this pattern survives review is that it is correct in every test that runs one request at a time.

02

Treating a timed-out write as a failed write

A timeout says the response did not arrive, not that the work did not happen; the server may well have committed and then lost the connection. Retrying a non-idempotent create after a timeout is the standard way to end up with two of something, and those duplicates land precisely when the system is already degraded and least able to absorb them. The discipline is to treat a timeout as unknown: either the write carries an idempotency key so the retry is safe by construction, or the client re-reads authoritative state before deciding what to do, and the interface says unknown rather than showing a failure that invites a second click.

03

Comparing floating-point values for equality, or holding money in them

Binary floating point cannot represent 0.1 exactly, so repeated addition drifts and an equality check fails on values that are mathematically equal. Store currency as integer minor units or a decimal type, and compare floats against a tolerance you chose for a stated reason.

04

Arguing past a hint

When the interviewer asks what happens for a particular input or floats a different data structure, stop and take it seriously; it is almost always a correction rather than idle curiosity. Talking over it converts a recoverable wrong turn into a data point about how you handle review.

Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.

10 technical prompts3 include a worked solution

Find the shortest path in a complex directed acyclic graph (DAG) repre…

medium
data structures and algorithms

Find the shortest path in a complex directed acyclic graph (DAG) representing multi-stage data pipelines.

Approach
  1. Name the brute-force solution and its complexity before improving on it.
  2. Restate the input: its shape, its size, and what is guaranteed about it.
  3. State the target complexity and say which constraint rules the naive version out.
Follow-up
  • What is the worst case, and how likely is it on real data?
  • Which test case would catch an off-by-one here?

Hold a tenant to a trailing sixty-second request limit

mediumWorked solution
sliding-windowtwo-pointerrate-limitingtenant-skew

The gateway must hold each tenant to R requests in any trailing 60 seconds, in aggregate across three regions and every pod, within a budget of under 10 ms added p99. Peak is 30,000 requests/second across 200,000 active tenants, and traffic is heavily skewed toward a handful of them. Give an exact single-process algorithm with its amortised per-request cost and its memory per tenant, then a bounded-memory approximation and the worst-case overshoot it actually admits. Say what the distributed version does when the counter store is unreachable.

Approach
  1. Exact, single process: a per-tenant deque of request timestamps. On arrival, pop from the front while front <= now - 60s, then admit if the remaining length is below R and push. Each timestamp is pushed once and popped once, so the cost is O(1) amortised. The O(R) version is the one that re-filters the whole deque on every request.
  2. Quote the memory. R = 1,000 across 200,000 active tenants is up to 2 x 10^8 timestamps at 8 bytes, about 1.6 GB, and that is the worst case rather than the mean, because the long tail of small tenants holds almost nothing. Skew helps you here and hurts you in the sharding decision.
  3. Bounded alternative, with its real bound stated: a fixed 60-second counter is O(1) memory but admits close to 2R across a 60-second span straddling a boundary. The weighted two-bucket estimate, prev * (60 - elapsed)/60 + cur, is better on smooth traffic but assumes the previous window's arrivals were uniform; an adversary packing them at the end of that window is undercounted and can still approach 2R. Say that rather than calling it exact.
  4. Token bucket is the usual gateway answer and a different contract: O(1) state per tenant (tokens, last_refill), a sustained rate, and a deliberate burst allowance equal to the bucket size. Choose it when a burst is acceptable and the log when the limit is contractual.
  5. Distributed: the limit is per tenant in aggregate, so a local bucket of R/N per pod is wrong in both directions under skew. A tenant landing on one pod is throttled at R/N, and a tenant spread evenly across pods exceeds R. The shared check must be a single atomic round trip, one script or one increment-and-compare, never read-then-write, and it must fit inside the 10 ms p99 budget.
  6. Decide the unavailable case in advance and write it down. Failing open keeps the product up and lets a tenant exceed its limit for the duration; failing closed converts a counter-store outage into a full outage. Most gateways fail open on rate limits and closed on authorisation, and those are two separate decisions made separately.
Worked solution 25 min
  1. Implement the deque version and instrument the per-request pop count, then confirm total pops equal total pushes over a run.
  2. Generate a burst that places R requests in the last 100 ms of one minute and R more in the first 100 ms of the next.
  3. Run that burst through the exact deque, a fixed 60-second counter, and the weighted two-bucket estimate, recording admissions in the trailing 60 seconds at every instant.
  4. Size the memory as R x active tenants x 8 bytes at R = 1,000 and 200,000 tenants, and compare it against what a token bucket would need.
EXPECTED RESULTThe exact deque never admits more than R in any trailing 60-second window. The fixed counter admits close to 2R across the boundary. The weighted estimate lands between the two on this burst and approaches 2R once the previous window's requests are packed at its end.
Follow-up
  • One tenant sends 40% of all traffic. What does that do to a single counter key, and what do you shard on instead?
  • Quotas rather than rate limits: the check is select used; if used < limit then insert. Name the isolation level that still permits the overshoot, and the two fixes.
  • How do you return an accurate Retry-After from the exact algorithm without a second scan?

Locate a billing reconciliation gap without rescanning ninety million events

hard
reconciliationdimensional-bisectionwatermarkshypothesis-testing

A tenant's sealed invoice total is 0.4% below the sum of its raw usage_event rows for the period. That tenant has 90 million events over 30 days in a table partitioned daily on ingested_at, and its rollups carry source_max_ingested_at, revision and sealed_at. Recomputing all 30 days from raw is correct, and you are not going to do it. Give the procedure that locates the divergent (workspace, sku, hour) cell, the cost of each probe, and the one query you run before any of it.

Approach
  1. Run the free query first. Sum raw quantity for the period restricted to ingested_at <= source_max_ingested_at of the sealed rollups, and compare that against the unrestricted sum. The rollup stores the watermark precisely so this can be answered without a scan. If the whole 0.4% sits above the watermark, nothing is broken: it is late data, it becomes an adjustment line, and the investigation ends in one query.
  2. Only if the gap survives that test do you bisect, and you bisect by dimension rather than by rows. Compare 30 per-day totals, then inside the offending day compare the 6 SKUs, then the workspaces, then the 24 hours. That is roughly 30 + 6 + W + 24 grouped probes, each an indexed range scan over one daily partition for one tenant, against O(N) per attempt for the naive re-fold.
  3. Quantify why naive is not merely slow but unusable mid-incident: at a generous 200,000 rows/second sequential, 90 million rows is about 7.5 minutes per attempt, you will want ten attempts, and every one competes for I/O on the same partitions live ingest is writing. The diagnostic worsens the backlog it is diagnosing.
  4. Before fetching each comparison, state what it would look like under each hypothesis. Two adjacent hours off by equal and opposite amounts is occurred_at versus ingested_at bucketing. A whole day offset by exactly N hours is a timezone applied at the wrong layer. A gap confined to one SKU in one workspace is an environment filter. The same (tenant_id, idempotency_key) present in two ingested_day partitions is the dedup horizon losing a retry that crossed midnight.
  5. Make the next bisection cheap by storing the aggregate you keep recomputing. A per-(tenant_id, ingested_day) count and quantity checksum turns step two from thirty probes into one read, and it is the same number the reconciliation job already produces.
  6. Whatever you find, the sealed period does not change value. The correction is an adjustment line pointing at the line it reverses, carrying its own source_rollup_watermark, because the original invoice is the evidence of what the customer was charged.
Follow-up
  • The gap is 0.4% in one direction on one day and 0.4% the other way the next day. What does that shape rule in, and what does it rule out?
  • How do you distinguish a duplicate from a restatement, given revision and recomputed_at on the rollup?
  • Ingest is still running while you investigate. What makes your two numbers comparable at all?

Day one measures instead of guessing, under a fixed rubric, and the remaining hours are allocated in proportion to the gaps before any studying begins. The allocation is deliberately not renegotiated midweek, because the area that feels worst on day three is usually the one that is moving.

Small steps. Visible outcomes.0 / 7 completed
ONE WEEK · YOUR PACE

Prepare, practise & reflect

One practical outcome each day. Spend longer where you need it.

0 / 7 done
01Diagnostic, scored before you study anything
  • Sit a 110-minute diagnostic in four blocks: forty-five minutes on two coding problems, twenty-five on one design prompt taken to interface and data model, twenty of short-answer fundamentals, and twenty delivering two behavioural answers aloud.
  • Score each block from 0 to 3 on a fixed rubric where 3 is correct and fluent, 2 is correct but slow or prompted, 1 is partially correct and 0 is stuck, grading the artifact rather than how the attempt felt.
  • Allocate days two to five in proportion to 3 minus each block's score, write the allocation down, and commit to leaving it alone.

Deliverable: A scored rubric and a fixed hour allocation for the rest of the week.

Practice prompt ↗Practice prompt ↗Worked solution ↗
02Largest gap: find the boundary rather than the subject
  • Split the weakest area into named sub-skills and rate each separately. For coding those are restating the problem, choosing the structure, stating the invariant, turning the invariant into loop bounds, handling empty and single-element input, and accounting for complexity out loud.
  • Attempt three items positioned just above where the rating drops off, and for each write the first move you failed to make.
  • Re-attempt one of them from blank four hours later with nothing open.

Deliverable: A sub-skill map with the two blocking sub-skills circled.

Practice prompt ↗Practice prompt ↗
03Drill the blocking sub-skill by repeating the shape
  • Do eight short repetitions of the same shape rather than eight different problems, so what gets practised is the pattern and not the puzzle.
  • State the rule you now hold in one sentence, then test it against a case built to break it, a sliding window over an array containing negative values, or a cache-aside read path whose invalidation message is dropped.
  • Have someone else read your one-sentence rule and find the precondition you left out.

Deliverable: One rule statement with its preconditions attached and one counterexample that would have caught the incomplete version.

Practice prompt ↗Practice prompt ↗
04Second gap, plus maintenance on the strongest area
  • Run the same sub-skill decomposition on the second-largest gap in half the time.
  • Spend twenty-five timed minutes on the block you scored highest, choosing the hardest item you can still finish rather than a warm-up.
  • Write whether each area fails you on recall, on setup, or on execution, and set the fix accordingly: repetition for recall, a written checklist for setup, timed work for execution.

Deliverable: A second sub-skill map plus a one-line failure diagnosis for each area.

Practice prompt ↗Practice prompt ↗Worked solution ↗
05The gap that is not a skill
  • Record one technical and one behavioural answer, then count two things in the playback: seconds before your first clarifying question, and sentences you began without knowing where they would end.
  • Practise saying that you do not know, followed by how you would find out, without letting it soften into a guess, and practise stating a complexity or an estimate before being asked for it.
  • Redeliver one answer under a hard ninety-second cap, which forces structure ahead of detail.

Deliverable: Two recordings with a counted reduction in time-to-first-question.

Practice prompt ↗Practice prompt ↗
06Retest under day-one conditions
  • Sit the same 110-minute structure with new prompts of comparable difficulty and score it on the identical rubric.
  • For any block that did not move, change the method rather than adding hours: a block stuck at 1 usually means the practice was too varied, not too short.
  • Write down which single block you would still lose the offer on.

Deliverable: A second scored rubric placed beside the first, with one named remaining risk.

Practice prompt ↗Practice prompt ↗
07Full loop under interview conditions
  • Run a sixty-minute mock over the two blocks that moved least, with an interviewer briefed to interrupt and change direction mid-answer.
  • Write the recovery script for going blank: restate the question, state your assumption, name the first thing you would check.
  • Say every rule from the week aloud without reading it, and cut any you cannot state in a single sentence, since a rule you have to reconstruct mid-answer will not survive an interruption.

Deliverable: A one-page card holding the recovery script and only the rules you could state from memory.

Practice prompt ↗Worked solution ↗

Expand any day for tasks and deliverables. Your progress is saved on this device.

Conflict answers where you were right and everyone came round are the weakest ones. Stronger: the evidence you went and collected, what would have changed your mind, and what you did in the weeks after the call went against you. Implementing a design you argued against, properly, is a specific and checkable behaviour.

Walk me through a complex technical project you owned from end to end.…

medium
behavioural and engineering judgement

Walk me through a complex technical project you owned from end to end. What tools did you use, and why?

Approach
  1. Give the blast radius: what could have broken, and what you measured.
  2. Close with what you would do differently, concretely.
  3. State the situation in two sentences and spend the rest on the reasoning.
Follow-up
  • How did you know your change caused the improvement?
  • What would you do differently if you ran that again?

Describe a time when a project did not go as planned. What challenges …

medium
behavioural and engineering judgement

Describe a time when a project did not go as planned. What challenges did you encounter, and what would you change if you had to do it again?

Approach
  1. Close with what you would do differently, concretely.
  2. Name the disagreement and how you resolved it with evidence.
  3. Give the blast radius: what could have broken, and what you measured.
Follow-up
  • What would you do differently if you ran that again?
  • What did you decide not to do, and why?

Resolve a review disagreement over a quota check

easy
code reviewisolation levelswrite skewdisagreement

A colleague's pull request enforces a per-tenant quota by selecting the current count and then inserting when it is under the limit. You flag it as a race. They reply that the transaction already runs at repeatable read, so the snapshot makes it safe, and the tests pass. Walk through taking that disagreement to a resolution: what you write in the review, what you demonstrate rather than assert, which fix you propose and why, and what you do if they still disagree after all of it.

Approach
  1. Answer the claim precisely instead of restating your objection, because they have made a specific technical argument. In PostgreSQL, repeatable read is snapshot isolation; this is write skew, which snapshot isolation permits by design. Both transactions read a count that is stable within their own snapshot, insert disjoint rows that the other cannot see, and both commit, so the limit is exceeded by exactly the concurrency.
  2. Demonstrate rather than cite. Two psql sessions, both BEGIN ISOLATION LEVEL REPEATABLE READ, both select the count, both insert, both commit: it succeeds. Repeat at SERIALIZABLE and the second commit fails with serialization_failure, SQLSTATE 40001. That takes two minutes, ends the argument without anyone conceding a position, and leaves an artefact for the next reviewer.
  3. Offer the options with their costs rather than a verdict. Serialisable plus a retry loop on 40001 is correct but obliges every caller to retry and degrades under contention. An increment-and-compare on a counter row — update tenant_quota set used = used + 1 where tenant_id = $1 and used < limit returning used — is safe even at read committed, because a blocked updater re-evaluates the WHERE clause against the row version it finally locks, and zero rows returned means full. A unique or exclusion constraint that makes the surplus write fail is the third.
  4. Name the plausible non-fix explicitly, since it is what usually gets merged instead: folding the count into the insert as insert ... select ... where (select count(*) ...) < limit is still racy under read committed, because the subquery cannot see the other transaction's uncommitted rows. It looks atomic and is not.
  5. Say what you do if they still disagree: escalate the decision rather than the disagreement. Attach the reproduction, hand it to the service owner or a third reviewer, and state that you will not block the merge if the owner accepts the risk knowingly — and that you want that acceptance written down.
  6. Close with the general lesson worth leaving in the review thread: a passing suite is weak evidence for a concurrency claim because it runs one request at a time. Ask for a test that runs two.
Follow-up
  • Write the counter-row version. Does your answer change if the quota counts child rows rather than a column?
  • Under serialisable, who performs the retry, and what does the API client see if the retry also fails?
  • This is the third disagreement with the same reviewer this month. What changes in how you review?
  • 01

    Walk me through a complex technical project you owned from end to end. What tools did you use, and why?

  • 02

    Describe a time when a project did not go as planned. What challenges did you encounter, and what would you change if you had to do it again?

  • 03

    A colleague's pull request enforces a per-tenant quota by selecting the current count and then inserting when it is under the limit. You flag it as a race. They reply that the transaction already runs at repeatable read, so the snapshot makes it safe, and the tests pass. Walk through taking that disagreement to a resolution: what you write in the review, what you demonstrate rather than assert, which fix you propose and why, and what you do if they still disagree after all of it.

PracHub interview preparation framework ↗
Is this an official Scale Ai interview guide?

No. It is PracHub's own research and practice material for the Software Engineer role at Scale Ai. Rounds and questions reflect what candidates have reported, not a process Scale Ai has published, and they change over time. Confirm the current format and scope with your recruiter.

PracHub interview research ↗
How difficult is the Software Engineer interview process at Scale Ai?

The process is highly rigorous and generally rated as difficult. It places a strong emphasis on practical coding, real-time debugging, and functional object-oriented design rather than purely theoretical algorithmic puzzles. Successful candidates typically spend significant time preparing for systems design and practical coding scenarios.

PracHub interview research ↗
What is the primary focus of the coding rounds?

While you will face standard algorithmic questions, Scale Ai heavily prioritizes practical software engineering. This means you will be evaluated on your ability to write clean, modular, and extensible Object-Oriented Design (OOD) code, design clean APIs, and debug existing codebases under time pressure.

PracHub interview research ↗
What is the company culture like for engineers?

The culture is highly ambitious, fast-paced, and intense. Engineers are given immense ownership and are expected to ship high-quality code quickly. It is an environment that rewards proactive problem-solvers who can navigate ambiguity and are passionate about the AI space.

PracHub interview research ↗
How long does the hiring process take?

Scale Ai moves quickly. The entire process, from the initial recruiter screen to the final offer decision, typically takes between two to three weeks. The virtual onsite is often split over two consecutive days to keep the candidate's energy levels high.

PracHub interview research ↗
Sources & methodology 3 sources ↗

No official company page is cited. Rounds and questions come from candidate reports and PracHub editorial material; each source shows the date it was read.