# **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** ```python # 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`](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** ```json { "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`**](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 --- **Ready to build the future of recruiting?** Start now: Fork our starter template → Build your agent → Submit ASAP