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https://github.com/amareshhebbar/hiresignal

Ranks 100K candidates against a Senior AI Engineer JD in ~35s on CPU — multi-signal scoring, honeypot detection, semantic embeddings. INDIA RUNS Hackathon Track 1.
https://github.com/amareshhebbar/hiresignal

candidate-ranking embeddings faiss hackathon india-runs india-runs-2026 information-retrieval machine-learning nlp python redrob sentence-transformers

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Ranks 100K candidates against a Senior AI Engineer JD in ~35s on CPU — multi-signal scoring, honeypot detection, semantic embeddings. INDIA RUNS Hackathon Track 1.

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README 

          



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**Intelligent Candidate Discovery & Ranking**



*INDIA RUNS Hackathon — Track 1 (Data & AI Challenge) — Redrob AI × Hack2Skill*



[![Python](https://img.shields.io/badge/Python-3.11+-3776AB?style=flat-square&logo=python&logoColor=white)](https://python.org)

[![Tests](https://img.shields.io/badge/Tests-10%20passed-22c55e?style=flat-square)](tests/)

[![Runtime](https://img.shields.io/badge/Runtime-~35s%20CPU-6366f1?style=flat-square)](#compute)

[![Candidates](https://img.shields.io/badge/Dataset-100K%20candidates-f59e0b?style=flat-square)](#)

[![Demo](https://img.shields.io/badge/Live%20Demo-HuggingFace%20Spaces-ff6b35?style=flat-square&logo=huggingface)](https://huggingface.co/spaces/AmareshHebbar/hiresignal)






---



## What it does



Ranks 100,000 candidate profiles against a Senior AI Engineer job description in **~35 seconds on CPU** — no GPU, no API calls, no network during ranking.



Goes well beyond keyword matching. Scores each candidate across four dimensions, detects ~85 honeypot/fraudulent profiles, and generates a specific natural-language explanation for every ranking decision.



```

$ make rank

[1/6] loading dataset/candidates.jsonl

      100,000 candidates loaded  (7.1s)

[2/6] honeypot detection

      85 flagged  (0.4s)

[3/6] scoring all candidates

      done  (10.6s)

[4/6] semantic refinement on top 500

      done  (11.0s)

[5/6] selecting top 100

[6/6] writing submission.csv

      done  (0.0s)



done  total=29.0s

  [  1] CAND_0041669  Recommendation Systems Engineer  8.0yrs

  [  2] CAND_0011687  Senior NLP Engineer  7.8yrs

  [  3] CAND_0064326  Search Engineer  7.6yrs

  [  4] CAND_0052682  NLP Engineer  6.6yrs

  [  5] CAND_0017960  Recommendation Systems Engineer  7.7yrs



$ make validate

Submission is valid.

```



---



## Demo



> **[Live sandbox on HuggingFace Spaces →](https://huggingface.co/spaces/AmareshHebbar/hiresignal)**



![HireSignal live demo](screenshots/hiresignal_4.png)



## Output



![Top 10 ranked candidates](screenshots/hiresignal_5.png)



## Pipeline



![Pipeline run](screenshots/hiresignal_1.png)



![Submission validated](screenshots/hiresignal_2.png)



![Tests passing](screenshots/hiresignal_3.png)



---



## Architecture



```

                        candidates.jsonl  (100,000 profiles)

                               │

                               ▼

              ┌────────────────────────────────┐

              │      HONEYPOT DETECTION        │

              │                                │

              │  expert skill + 0 months use   │

              │  career months > 1.6× YoE      │

              │  9+ expert skills listed       │

              │  start_date before 2005        │

              │  is_current=True + end_date    │

              │                                │

              │  → 85 flagged, score = 0.001   │

              └────────────────┬───────────────┘

                               │ 99,915 clean candidates

                               ▼

        ┌──────────────────────────────────────────────┐

        │              MULTI-SIGNAL SCORING            │

        │                                              │

        │  ┌─────────────────┐  ┌──────────────────┐  │

        │  │  SKILL MATCH    │  │ CAREER TRAJECTORY │  │

        │  │    40% weight   │  │    35% weight     │  │

        │  │                 │  │                   │  │

        │  │ proficiency ×   │  │ title quality     │  │

        │  │ duration ×      │  │ company type      │  │

        │  │ endorsements ×  │  │ YoE band (6-8yr)  │  │

        │  │ assessments     │  │ prod AI evidence  │  │

        │  └─────────────────┘  │ location          │  │

        │                       └──────────────────┘  │

        │  ┌──────────────────────────────────────┐    │

        │  │      BEHAVIORAL SIGNALS  25% weight  │    │

        │  │                                      │    │

        │  │  last_active_date    response_rate   │    │

        │  │  notice_period       github_activity │    │

        │  │  profile_completeness  verifications │    │

        │  └──────────────────────────────────────┘    │

        └─────────────────────┬────────────────────────┘

                              │ sorted, top 500

                              ▼

              ┌────────────────────────────────┐

              │    SEMANTIC REFINEMENT         │

              │    paraphrase-MiniLM-L6-v2     │

              │                                │

              │  JD text → embedding           │

              │  candidate profile → embedding │

              │  cosine similarity → +20% blend│

              └────────────────┬───────────────┘

                               │ re-sorted

                               ▼

              ┌────────────────────────────────┐

              │       TOP 100 + REASONING      │

              │                                │

              │  per-candidate explanation     │

              │  references actual facts       │

              │  honest about gaps             │

              └────────────────────────────────┘

                               │

                        submission.csv

```



---



## Scoring model



| Component | Weight | What it measures |

|---|---|---|

| Skill match | 40% | JD-required skills weighted by proficiency × duration × endorsements × assessment scores |

| Career trajectory | 35% | Title quality, company type, YoE band, production AI evidence, location |

| Behavioral signals | 25% | Availability, responsiveness, engagement — treats inactivity as an availability gate |

| Semantic similarity | +20% blend | Sentence-transformer cosine sim on top-500 only — stays within 5-min budget |



### Why behavioral signals are a gate, not a bonus



The JD explicitly says:



> *"A perfect-on-paper candidate who hasn't logged in for 6 months and has a 5% recruiter response rate is, for hiring purposes, not actually available."*



So a candidate with great skills but 200+ days inactive gets significantly down-weighted — not penalized slightly.



### Why pure consulting careers get penalized



The JD explicitly names TCS, Infosys, Wipro, Accenture, Cognizant, Capgemini as patterns they want to move away from. A candidate whose entire career is at these firms gets a `0.2` company score regardless of their skill list.



---



## Honeypot detection



The dataset contains ~85 impossible profiles designed to catch naive rankers. We detect them with five logical impossibility checks:



```python

# 1. Expert proficiency + 0 months of actual use on 2+ skills

expert_zero = [s for s in skills

               if s["proficiency"] == "expert" and s.get("duration_months", 1) == 0]

if len(expert_zero) >= 2:

    return True



# 2. Sum of career months > 1.6× claimed years_of_experience

if total_career_months > yoe * 12 * 1.6 and total_career_months > 36:

    return True



# 3. 9+ skills listed as "expert" — keyword stuffer pattern

if sum(1 for s in skills if s["proficiency"] == "expert") >= 9:

    return True



# 4. Job start_date before 2005 for someone with < 20 YoE

if yoe < 20 and any(int(job["start_date"][:4]) < 2005 for job in career):

    return True



# 5. is_current=True but end_date is set — logical contradiction

if any(j.get("is_current") and j.get("end_date") for j in career):

    return True

```



All honeypots score `0.001` and never appear in top-100. The original code had a sixth rule (`skill.duration_months > yoe * 12`) that was incorrectly flagging 9,231 legitimate candidates — caught and fixed during development.



---



## Project structure



```

hiresignal/

│

├── rank.py                     entry point (7 lines)

├── Makefile                    make rank / validate / test / setup

├── requirements.txt

├── app.py                      Gradio demo for HuggingFace Spaces

│

├── src/

│   ├── pipeline.py             orchestrates all 6 steps

│   ├── loader.py               reads candidates.jsonl line by line

│   ├── jd.py                   JD constants — skill lists, title sets, cities

│   │

│   ├── filters/

│   │   └── honeypot.py         5 impossibility checks

│   │

│   ├── scoring/

│   │   ├── skills.py           skill match scorer

│   │   └── career.py           career trajectory scorer

│   │

│   ├── signals/

│   │   └── behavioral.py       23 Redrob platform signals

│   │

│   ├── embed/

│   │   └── encoder.py          sentence-transformer on top-500

│   │

│   └── output/

│       ├── writer.py           CSV output + validation asserts

│       └── reasoning.py        per-candidate reasoning strings

│

├── scripts/

│   ├── download_model.py       one-time model download

│   └── check_output.py         inspect top-10 with full profile details

│

├── tests/

│   ├── test_honeypot.py        5 tests

│   └── test_scoring.py         5 tests

│

└── dataset/

    ├── sample_candidates.json  first 50 candidates

    ├── sample_submission.csv   format reference

    ├── validate_submission.py  official format validator

    └── candidate_schema.json   JSON schema for candidate objects

```



---



## Quickstart



```bash

git clone https://github.com/amareshhebbar/hiresignal

cd hiresignal



make setup                    

source .venv/bin/activate



make model                    

make rank                    

make validate                

make test                    

```



Manual:



```bash

python rank.py --candidates dataset/candidates.jsonl --out submission.csv

python dataset/validate_submission.py submission.csv

```



---



## Compute profile 



```

Platform   Fedora Linux, 8-core CPU

Memory     ~2.5 GB peak

Runtime    ~29s (rule-based only) / ~35s (with cached embeddings)

Network    none during ranking

GPU        not used

```



Pre-computation (`make model`) downloads the sentence-transformer model (~90MB) once to `.model_cache/`. The ranking step itself has zero network dependency — satisfies the submission spec constraint.



---



## Tests



```bash

make test



# ===== test session starts =====

# tests/test_honeypot.py::test_clean_passes               PASSED

# tests/test_honeypot.py::test_expert_zero_months         PASSED

# tests/test_honeypot.py::test_career_months_inflated     PASSED

# tests/test_honeypot.py::test_too_many_expert_skills     PASSED

# tests/test_honeypot.py::test_is_current_with_end_date   PASSED

# tests/test_scoring.py::test_ai_engineer_scores_above_zero         PASSED

# tests/test_scoring.py::test_more_skills_scores_higher             PASSED

# tests/test_scoring.py::test_disqualifying_title_gets_very_low     PASSED

# tests/test_scoring.py::test_ideal_yoe_band_boosts_career          PASSED

# tests/test_scoring.py::test_india_location_preferred              PASSED

# ===== 10 passed in 0.07s =====

```



---



## Submission



| Field | Value |

|---|---|

| Track | Track 1 — Data & AI Challenge |

| Hackathon | INDIA RUNS — Redrob AI × Hack2Skill |

| Deadline | July 2, 2026 |

| Reproduce | `python rank.py --candidates ./dataset/candidates.jsonl --out ./submission.csv` |

| Sandbox | [huggingface.co/spaces/AmareshHebbar/hiresignal](https://huggingface.co/spaces/AmareshHebbar/hiresignal) |



---






Built by [G V Amaresh](https://linkedin.com/in/gvamaresh)  ·  [HuggingFace](https://huggingface.co/AmareshHebbar)  ·  [LinkedIn](https://linkedin.com/in/gvamaresh)






< docs pass retry: 2026-07-03T04:44:14Z -->