Redesign a backend hiring loop for candidates who use AI: one AI-allowed round with a real fluency rubric, one AI-free fundamentals round, and a published candidate policy. One of the three current interview problems dissolves under a single prompt — you must prove which one by running the attack yourself and logging the result.
Daniel Okoye runs backend hiring at Ledgerline, a fictional 120-person fintech. Last quarter two hires aced the take-home and struggled from week one; a debrief revealed both had AI-generated their submissions — which the loop neither forbade nor tested for. Daniel's conclusion is the right one: the problem is not cheating candidates, it is a loop that measures a world that no longer exists. Your job is his job — rebuild the loop so AI-assisted work is assessed openly where it reflects the job, and fundamentals are assessed where AI cannot sit in the room.
CURRENT LOOP | Backend Engineer II, Ledgerline (unchanged since 2022)
R1 Recruiter screen (30 min) - motivation, salary, visa
R2 Take-home (unproctored, 4 hrs) - "Build a rate limiter service"
R3 Onsite coding (60 min) - Problem A or B, no external tools
R4 System design (60 min) - Problem C
R5 Behavioral (45 min) - past projects, conflict, ownership
AI policy: none stated. Take-home email says "your own work."
COMPETENCY MAP | what the role actually requires (from job analysis)
C1 Decompose ambiguous requirements into testable slices
C2 Reason about failure modes, concurrency, and data integrity
C3 Read and debug unfamiliar code under time pressure
C4 Validate outputs against requirements before shipping
C5 Communicate tradeoffs to non-authors
C6 Use AI tooling productively: prompt, verify, reject bad output
THE THREE PROBLEMS currently in rotation
Problem A: "Given this 80-line service handler and a failing
integration test log, find why retries double-charge customers
under concurrent requests. Artifacts: handler summary, test log,
payment-provider API notes."
Problem B: "Implement an in-memory rate limiter supporting fixed
window and sliding window, with tests. Language of choice."
Problem C: "Design a settlement-reconciliation pipeline for 2M
transactions/day across three banking partners with late-arriving
data. Whiteboard discussion."
Run the one-prompt attack first. Before redesigning anything, paste each of the three problems — exactly as written — into your AI as a single prompt, no follow-ups allowed. For each, log: problem, prompt used, verbatim summary of what came back, and your verdict — SOLVED (output would pass the round with light editing), PARTIAL (usable scaffold, real gaps), or RESISTANT (output cannot pass without knowledge the AI lacks). This log is a graded artifact; be honest about verdicts.
Classify each problem's failure mode. For the problem marked SOLVED, write two sentences on why it dissolved: it is self-contained, fully specified, and famous — the model has seen hundreds of variants. For the survivors, name what protected them: dependence on artifacts requiring interpretation, or interactive tradeoff dialogue an offline answer cannot fake.
Draft the new loop structure. Prompt: "Redesign this five-round backend loop so it contains exactly one AI-allowed round assessing competency C6 alongside C1/C4, and one AI-free round assessing C2/C3 fundamentals live. Keep total candidate time under 5 hours. For each round: what is assessed, AI allowed or not, and why." Then edit the output yourself — the AI will propose a reasonable skeleton but will not know which problem died in step 1. Retire or rebuild that problem explicitly.
Build the AI-fluency rubric for the AI-allowed round. Define observable behaviors at three levels (below bar / at bar / strong) for each of: prompting (does the candidate give the AI context and constraints, or vague one-liners?), output validation (do they test and inspect before accepting, or paste and pray?), and debugging AI suggestions (when the AI's output contains a flaw, do they find it or defend it?). Require the round to include at least one moment where the AI's output is subtly wrong — seeded by the interviewer if it does not occur naturally.
Harden the AI-free round. Specify format (live, shared screen, interviewer-driven artifacts), what is assessed (C2, C3 — reading unfamiliar code, reasoning about concurrency and failure), and the fairness rationale you will publish: fundamentals are tested live not because AI is banned at work, but because the job includes moments where you must reason without a crutch — incident calls, reviewing AI output itself.
Write the candidate-facing AI policy. One page, published in the scheduling email: which rounds allow AI (and that its use is expected and assessed there), which rounds do not, what happens if AI is used in a no-AI round, and why the loop is built this way. Plain language, no legalese, no gotcha framing.
Red-team your own redesign. Prompt: "You are a candidate determined to pass this loop using AI covertly wherever it is banned. Given this loop spec, where would you attack?" For each credible attack, either accept the risk in writing or adjust the design. Take-homes without a follow-up defense conversation should not survive this step.
Assemble the artifact pack. Final deliverables: loop spec (rounds, competencies, AI stance each), fluency rubric, candidate policy, solve-attempt log from step 1, and a five-line change memo to your recruiting partner summarizing what changed and why.
The strongest signal in an AI-allowed round is not whether the candidate's code works — it is what they do in the thirty seconds after the AI answers. Watch for the pause, the test, the pushback. Hire the pause.
A complete lab has all of the following:
A hiring loop is a measurement instrument, and AI changed what it measures. Test every problem against the one-prompt attack, assess AI fluency openly where the job uses it, and protect one live round where reasoning has nowhere to hide.