LinkedIn Developer: Multi-Agent Job Matching
Arindam Majumder shared a multi-agent project that finds YC job openings based on LinkedIn profiles. Task: Suggested implementation: A multi-agent workflow analyzes a user's LinkedIn profile for experience and skills, then scrapes YC job boards to generate a ranked list of matching positions with application links. The system uses OpenAI Agents SDK for orchestration, Bright Data MCP for web scraping, and Nebius AI Studio for inference, presented via a Streamlit UI. The human role involves providing profile URLs, verifying the relevance of suggested jobs, and manually applying to positions.; tools: OpenAI Agents SDK, Bright Data MCP, Nebius AI Studio, Streamlit. Human role: Job seekers provide their profile links, judge whether positions are suitable, and apply on their own.; limitations: This is a personal project demonstration, not an independently evaluated recruitment service. Users must verify job timeliness, information accuracy, and compliance with data collection permissions for LinkedIn and YC. Integration requires API keys for Bright Data and Nebius, and the workflow's reliability depends on the stability of external scraping endpoints..
Tools
OpenAI Agents SDK, Bright Data MCP, Nebius AI Studio, Streamlit
Published on Agent Work Atlas:
Overview
Arindam Majumder shared a multi-agent project that finds YC job openings based on LinkedIn profiles.
Workflow
Suggested implementation: A multi-agent workflow analyzes a user's LinkedIn profile for experience and skills, then scrapes YC job boards to generate a ranked list of matching positions with application links. The system uses OpenAI Agents SDK for orchestration, Bright Data MCP for web scraping, and Nebius AI Studio for inference, presented via a Streamlit UI. The human role involves providing profile URLs, verifying the relevance of suggested jobs, and manually applying to positions.
Reported result
The author demonstrated the generation of profile summaries, job lists, and application links. No metrics regarding job-seeking success rates, match accuracy, or time savings were reported. The outcome is limited to a functional proof-of-concept showing the workflow's ability to retrieve and format data.
Human role
Job seekers provide their profile links, judge whether positions are suitable, and apply on their own.
Limitations
This is a personal project demonstration, not an independently evaluated recruitment service. Users must verify job timeliness, information accuracy, and compliance with data collection permissions for LinkedIn and YC. Integration requires API keys for Bright Data and Nebius, and the workflow's reliability depends on the stability of external scraping endpoints.
Try it
Act as a developer building a job matching prototype. Use OpenAI Agents SDK, Bright Data MCP, and Nebius AI Studio to create a workflow that takes a [replaceable LinkedIn Profile URL] and outputs matched YC jobs. Steps: 1. Initialize agents to scrape profile data and YC job listings. 2. Match skills to job requirements. 3. Generate a ranked list with apply links. 4. Display results in a Streamlit interface. First, perform a dry run with a sample profile to verify data extraction. Require human confirmation of the top 3 matches for relevance before scaling. Measure success by the percentage of manually verified relevant matches. Handle missing API keys or scraping errors by logging specific failures. Do not claim this is a tested production system; label it as a proof-of-concept.
Source reports are not independently verified. Publication date is the catalog publication date, not the original article date.