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7a9cfa1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | # Problem & Evaluation Mechanics
> Part of the MochiRank docs. See [architecture/spec.md](../architecture/spec.md) for the system design.
## 1. Problem Restatement (Read Between the Lines)
The JD is a 2000-word essay for **Senior AI Engineer β Founding Team** at Redrob AI.
The hidden note at the bottom of the JD (for hackathon participants) is the real brief:
> "The right answer involves reasoning about the **gap between what the JD says and what the JD means**.
> A Tier 5 candidate may not use the words 'RAG' or 'Pinecone' but if their career shows they built
> a recommendation system at a product company, they're a fit. A perfect-on-paper candidate who hasn't
> logged in for 6 months and has a 5% recruiter response rate is not actually available."
Four trap types are explicitly built into the 100K dataset:
- **Keyword stuffers** β skills list has every AI keyword, but career history doesn't support them
- **Plain-language Tier 5s** β great candidate, zero buzzwords in their profile
- **Behavioral twins** β near-identical profiles, differ only in `redrob_signals`
- **~80 honeypots** β internally inconsistent profiles (>10% honeypot rate in top-100 = disqualification)
These traps map directly to architectural decisions. Pure embedding cosine similarity fails all four.
## 2. Evaluation Mechanics (What Actually Scores Points)
```
composite = 0.50 Γ NDCG@10 + 0.30 Γ NDCG@50 + 0.15 Γ MAP + 0.05 Γ P@10
```
**50% of the score is the top 10.** Find the 5β10 genuinely great candidates out of 100K.
Volume of mediocre-middle candidates barely moves the needle. Optimize for precision at the top.
**Stage 3 reproduction kills most submissions.** Must reproduce inside Docker: 5 min wall-clock,
16 GB RAM, CPU only, zero network. Plan for this from day one.
**Stage 4 reasoning checks** (if you reach top-N) sample 10 rows and check for:
specificity, JD connection, honest concerns, no hallucination, variation, rank-consistency.
If reasoning is mechanically derived from the model's actual decision, all 6 checks pass automatically.
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