project_description.md

Synapse Annual First Ever AI Hackathon - Sourcing Agent Challenge

Deadline: Monday 7 PM PST

Website: [www.synapsehire.com](http://www.synapsehire.com)

🚀 Overview

Build an autonomous AI agent that sources LinkedIn profiles at scale, scores candidates using our fit score algorithm, and generates personalized outreach - all in 2-3 hours using Cursor.

This isn't a typical coding challenge. We want to see if you can build what we actually build at Synapse.

🌍 Why This Is Special

We will promote your win through our company and high-profile personal LinkedIn pages to:

• Hundreds of our clients, including hiring managers and startup founders
• Top VCs and investors across the U.S. who rely on Synapse to build their founding teams
• 10s of thousands of other hiring managers and potential future connections
• Our SRN recruiter network of 1100+ professionals, many of whom can connect you to incredible job and internship opportunities

This isn't just a coding challenge — it's your fast track to visibility, credibility, and opportunity.

💰 Prizes

Top 2 Winners Each Receive:

• $500 cash prize
• 2-month paid internship ($750/month = $1,500 total)
• Work directly with PhDs and top AI engineers
• Build production AI systems used by 1000s of recruiters and companies
• Strong potential for full-time offer post-graduation

🎯 The Challenge

Build a LinkedIn Sourcing Agent that:

1. Finds LinkedIn Profiles
   • Takes a job description as input
   • Searches for relevant LinkedIn profile URLs
   • Extracts basic candidate data from search results
2. Scores Candidates
   • Implements our fit score rubric (provided below)
   • Rates candidates 1-10 based on job match
   • Shows scoring breakdown
3. Generates Outreach
   • Creates personalized LinkedIn messages using AI
   • References specific candidate details
   • Maintains professional tone
4. Handles Scale
   • Can process multiple jobs simultaneously
   • Manages rate limiting intelligently
   • Stores minimal data (just URLs + scores)

🏆 Bonus Points

• Multi-Source Enhancement: Combine LinkedIn data with GitHub, Twitter, or personal websites to improve fit scoring
• Smart Caching: Implement intelligent caching to avoid re-fetching
• Batch Processing: Handle 10+ jobs in parallel
• Confidence Scoring: Show confidence levels when data is incomplete

⚙️ Technical Requirements

Required Stack

• Development: Must use Cursor
• Language: Python or TypeScript
• LLM: Any (Gemini, Claude, etc.)
• Data Storage: Minimal (PostgreSQL, SQLite, or even JSON)

Required Features

# 1. Job Input
job_description = "Senior Backend Engineer at fintech startup..."

# 2. Candidate Discovery
candidates = agent.search_linkedin(job_description)
# Returns: [{"name": "John Doe", "linkedin_url": "...", "headline": "..."}]

# 3. Fit Scoring
scored_candidates = agent.score_candidates(candidates, job_description)
# Returns: [{"name": "...", "score": 8.5, "breakdown": {...}}]

# 4. Message Generation
messages = agent.generate_outreach(scored_candidates[:5], job_description)
# Returns: [{"candidate": "...", "message": "Hi John, I noticed..."}]

Example Architecture

Input Job → Search LinkedIn → Extract Profiles → Score Fit → Generate Messages
     ↓                              ↓                ↓              ↓
   Queue → RapidAPI/Scraping → Parse Data → Fit Algorithm → Gemini

📊 Fit Score Rubric (Simplified)

Use this scoring framework:

Education (20%)

• Elite schools (MIT, Stanford, etc.): 9-10
• Strong schools: 7-8
• Standard universities: 5-6
• Clear progression: 8-10

Career Trajectory (20%)

• Steady growth: 6-8
• Limited progression: 3-5

Company Relevance (15%)

• Top tech companies: 9-10
• Relevant industry: 7-8
• Any experience: 5-6

Experience Match (25%)

• Perfect skill match: 9-10
• Strong overlap: 7-8
• Some relevant skills: 5-6

Location Match (10%)

• Exact city: 10
• Same metro: 8
• Remote-friendly: 6

Tenure (10%)

• 2-3 years average: 9-10
• 1-2 years: 6-8
• Job hopping: 3-5

🛠️ Resources We Provide

Use the role below for your challenge:

We're recruiting for a Software Engineer, ML Research role at Windsurf (the company behind Codeium) - a Forbes AI 50 company building AI-powered developer tools. They're looking for someone to train LLMs for code generation, with $140-300k + equity in Mountain View.

This is perfect for the challenge because Windsurf builds AI coding assistants (like Cursor!), so you'll be sourcing candidates who understand exactly what you're building with.

Job Description To Use: https://app.synapserecruiternetwork.com/job-page/1750452159644x262203891027542000

LinkedIn Search Options

1. Google Search: site:linkedin.com/in "backend engineer" "fintech" "San Francisco"
2. RapidAPI: Fresh LinkedIn Data API (free tier available)
3. Direct parsing: Extract from search result snippets

Sample Output Format

{
  "job_id": "backend-fintech-sf",
  "candidates_found": 25,
  "top_candidates": [
    {
      "name": "Jane Smith",
      "linkedin_url": "linkedin.com/in/janesmith",
      "fit_score": 8.5,
      "score_breakdown": {
        "education": 9.0,
        "trajectory": 8.0,
        "company": 8.5,
        "skills": 9.0,
        "location": 10.0,
        "tenure": 7.0
      },
      "outreach_message": "Hi Jane, I noticed your 6 years..."
    }
  ]
}

📋 Submission Requirements

1. GitHub Repository with your code
2. README with setup instructions
3. Demo Video (3 minutes max) showing:
   • Running your agent on a job
   • Candidates being discovered and scored
   • Generated outreach messages
4. Brief Write-up (500 words max):
   • Your approach
   • Challenges faced
   • How you'd scale to 100s of jobs
5. Bonus: Share an api link created using FastAPI hosted on huggingface:

• which takes job description as input and returns top 10 candidates for that job along with there personalized outreach message.
• The outreach message should highlighting there profile's key characteristics and how it matches with this job all in json format.

⏰ Timeline

• Submit by: Monday, June 30, 2025 @ 7:00 PM PST
• Winners Announced: within 24 hours after deadline

📝 How to Submit

Fill out submission form: https://forms.gle/v4byfXiGXFej5heq6

❓ FAQ

Q: Can I use web scraping libraries? A: Yes, any method to get LinkedIn URLs/data is fine.

Q: What if I can't get full profile data? A: Work with what you can get. We care more about your approach than perfect data.

Q: Should I worry about rate limiting? A: Basic rate limiting awareness is good. Don't overthink it for the MVP.

Q: Can I use multiple LLMs? A: Yes, use whatever combination works best.

Q: What about LinkedIn ToS? A: This is an educational challenge. Use public data responsibly.

💡 Tips for Success

• Start Simple: Get basic search → score → message working first
• Use Cursor AI: Let it help you write boilerplate quickly
• Focus on the Pipeline: We care more about architecture than perfect accuracy
• Show Your Thinking: Comment your code, explain decisions
• Make it Runnable: We should be able to clone and run your code easily

🤝 About the Internship

What You'll Work On:

• Production AI agents handling 10,000+ candidates/month
• Real-time matching algorithms
• Distributed scraping systems
• LLM optimization at scale

Who You'll Work With:

• AI engineers from top companies
• Researchers published in top conferences
• Full-stack engineers building at scale

Location: Fully remote Commitment: 2 month contract Start Date: this week

🚨 Final Notes

• This is exactly what we build at Synapse
• The best solutions will actually be integrated into our platform
• We're looking for builders who can ship, not perfect code
• Using Cursor effectively is a key skill we value

Questions? email srn@synapserecruiternetwork.com

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