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Hiring at volumeA first-round interview for everyone who applies.
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Use cases/Technical interviews

A coding task, then the why

Tracy can introduce a coding task and probe the trade-offs behind the answer.

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Engineering teamsAI labsTech hiring platforms
Coding task · Remove duplicate eventsLive 18:05
1def dedupe(events):
2 seen = set()
3 out = []
4 for e in events:
5 if e.id in seen:
6 continue
7 seen.add(e.id)
8 out.append(e)
9 return out
10
11# 2M events a day, ids are UUIDs
TracyThat works. The set grows with every event. What happens on day thirty?
TomaszMemory keeps climbing. I would keep a window of ids, or a Bloom filter.
Tracy · Follow-upA Bloom filter can say yes when the answer is no. Which events would you drop by mistake?
Why it is hard today

The first round is where the time goes.

  • A passing test case says nothing about how the code holds up at scale.
  • Take-home tests are easy to outsource.
  • Senior engineers lose afternoons to first-round screens.
How Tracy runs it

Three steps. No calendar.

  1. Pick the skills

    Choose skills as tags and set the style from behavioural to technical.

  2. Tracy runs the task and the questions

    She asks about the code that was written, not a generic follow-up.

  3. Review with the signals

    Tab changes, an external screen and long pauses are marked on the recording.

What you get

Evidence behind every result.

  • Probing on trade-offsMemory, failure modes, what breaks at ten times the load.

  • Questions from the CV“Your CV mentions Kafka. What did you change in the consumer?”

  • Integrity signalsEvidence for a reviewer, with timestamps.

  • Engineer hours savedYour team joins at the round where it matters.

Result · Nina Alvarez · Senior data engineerResult ready
Assessment per question
No tab changesOne screenFace in frame
Index for the ledger table

Chose the index from measured read latency, and named the write cost without being asked.

04:1204:51
A migration that went wrong

Owned the rollback. Gave the order of steps and what she changed in the runbook after.

09:30
Retries from a payment provider

Idempotency keys on write. Did not cover what happens when the key store is down.

14:02Gap to probe
04:12
04:09Reads were the bottleneck, so we went with a B-tree on account and date.
04:12About four times faster on reads. Writes slowed by a fifth.
04:20We accepted that because writes were batched overnight anyway.

More use cases

Expert vetting

Vet experts in their own field

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Applications

Interview every applicant, not the shortlist

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Hiring at volume

Everyone who applies gets an interview

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Tracy AI Interviewer

Video interviews that follow up in the person's own field, with evidence behind every result.

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