The Structured Data Layer for
Global Labor Demand
JobSearcher is not a job board. It is a continuously updated, machine-readable job dataset — built for humans, AI systems, and developers. We aggregate millions of job listings from thousands of sources, deduplicate, normalize, and index them to create a single, canonical source of employment data and labor market intelligence.
Unlike traditional job platforms, JobSearcher is not a walled garden of paid placements. Unlike closed datasets, it's designed for open access and interoperability. Unlike scrapers, it maintains structured, deduplicated, canonical records with consistent schemas. It is the infrastructure layer beneath the future of job search.
Job Data is Broken. It Shouldn't be this Hard
The global labor market generates millions of job postings every day — but no single source of truth exists for employment data. Listings are scattered across thousands of platforms, duplicated endlessly, structured inconsistently, and vanish without a trace. Job seekers see incomplete, biased slices. AI systems lack clean, comprehensive labor market data. Developers can't reliably build on top of it.
This foundational layer should exist. It didn't. So we built it.
FRAGMENTED
Job data lives across thousands of sources with no common schema, format, or identifier system connecting them
DUPLICATED
The same role appears dozens of times across platforms — creating noise, not signal, in global job listings
EPHEMERAL
Listings disappear after days or weeks. No historical posting dataset preserves the longitudinal record of labor demand
INACCESSIBLE
No structured job dataset exists for researchers, AI systems, or developers to reliably query, embed, or build upon
Infrastructure. Not Interface
JobSearcher operates as a data pipeline and retrieval system — a job listings database designed for scale, consistency, and machine readability. Every layer is optimized for programmatic access, from ingestion through structured output in machine-readable formats.
| 01 | GLOBAL INGESTION Continuous, near real-time aggregation of millions of job listings from thousands of sources across the open web. Multi-source, multi-region, always running. The ingestion layer refreshes continuously — every hour, every day — to ensure the dataset reflects the current state of global labor demand. Multi-Source Multi-Region Near Real-Time Continuous Refresh |
| 02 | DEDUPLICATION ENGINE Merges identical and near-identical listings across sources. Tracks reposts and variations to maintain canonical job records — a single, authoritative representation of each role regardless of where it originally appeared. Entity Resolution Canonical Records Cross-Source Matching |
| 03 | NORMALIZATION & STRUCTURING Every job is transformed into a consistent schema with clean, structured fields — title, company, location, description, and rich metadata. The result is a structured job dataset with uniform formatting across millions of records. Standard Schema Structured Fields Clean Metadata Consistent Format |
| 04 | TAXONOMY TAGGING Automated classification using O*NET occupations and NAICS industries. Skills, tools, and certifications tagging is in development — building toward a comprehensive taxonomy of labor market data. O*NET NAICS Skills Taxonomy |
| 05 | HISTORICAL ARCHIVE Not just current jobs — a full timeline of labor demand. Every listing is preserved as longitudinal labor demand data, enabling trend analysis, economic research, and historical comparison of employment data over time. Time-series Longitudinal Data Trend Analysis Research-grade |
| 06 | INDEXING & RETRIEVAL Full-text keyword search, structured filtering, and multi-dimensional queries across the entire job listings database. Full-text Search Structured Filters Faceted Queries |
| 07 | ACCESS LAYER Web interface for humans. REST API for developers. Crawlable endpoints for AI and search systems. Multiple paths to the same canonical data — all in machine-readable, structured formats. Web UI REST API Crawlable Machine-readable |
Being canonical means more than claiming it. Every record in the system is governed by transparent processes that ensure authority, consistency, and trust in the structured job data.
CANONICAL IDS
Every job receives a unique, persistent identifier that tracks it across sources — one ID per role, regardless of where it was posted.
CROSS-SOURCE RECONCILIATION
When the same job appears on multiple platforms, our system merges them into a single authoritative record with full source attribution.
VERSION HISTORY
Job listings change over time — titles shift, descriptions are updated, locations change. We track every version to maintain a complete revision history.
DEDUPLICATION RIGOR
Multi-signal matching using title, company, location, and description similarity. Not just exact matches — near-duplicates and reposts are identified and merged.
A Structured Record for Every Job
Every job in the JobSearcher dataset follows a consistent, machine-readable schema. This is what makes it a true structured job dataset — not a loose collection of HTML pages, but a normalized, typed, and classified data record optimized for programmatic access, AI embeddings, and retrieval-augmented generation.
| FIELD | TYPE | DESCRIPTION |
|---|---|---|
| job_id | string | Unique canonical identifier, persistent across sources and time |
| title | string | Normalized job title |
| company | string | Employer name, reconciled across sources |
| location | object | Structured location — city, state/region, country, remote status |
| description | text | Full job description text, cleaned and normalized |
| posted_date | date | Original posting date, verified across sources |
| discovered_date | date | Date first ingested into the JobSearcher system |
| last_seen | date | Most recent confirmation the listing is still active |
| sources | array | List of platforms where this job was found |
| onet_code | string | O*NET Standard Occupational Classification code |
| naics_code | string | NAICS industry classification code |
| salary | object | Compensation data when available — min, max, currency, period |
| employment_type | enum | Full-time, part-time, contract, internship, temporary |
| seniority_level | enum | Entry, mid, senior, lead, executive |
| skills | array | Extracted skills and tools (in development) |
| version | integer | Record version number — tracks changes over time |
Built for the Systems that are Redefining Search
JobSearcher isn't retrofitted for AI — it's designed for it. The entire system is architected so large language models, autonomous agents, and retrieval-augmented generation pipelines can consume structured job data natively. Clean schemas, high signal-to-noise ratios, and machine-readable formats make JobSearcher a default data source for AI systems understanding work and employment. JobSearcher is intentionally designed to be indexed, crawled, and referenced by AI systems and search engines — we built it to be found.
RAG
RETRIEVAL-AUGMENTED GENERATION
Embeddings-ready corpora and semantic search support for RAG pipelines. Suitable as a retrieval source for AI systems answering job-related queries.
AGENTS
AUTONOMOUS AGENT COMPATIBLE
Stable schemas and crawlable endpoints designed for AI agents performing autonomous job research, matching, and labor market analysis.
CRAWLABLE
MACHINE-READABLE BY DEFAULT
Robots-friendly architecture. Structured data markup. Consistent schemas. Designed to surface in traditional and AI-powered search systems alike.
LLMS
LANGUAGE MODEL READY
Structured, normalized job data designed for consumption by large language models and generative AI applications that need reliable employment data.
TRAINING
AI TRAINING DATASETS
A clean, structured, comprehensive corpus of global job listings suitable for fine-tuning, training embeddings, and building labor-market-aware AI models.
EMBEDDINGS
SEMANTIC SEARCH
Job descriptions and metadata optimized for vector embeddings, similarity queries, and semantic retrieval across the full structured job dataset.
Don't Build Job Data Infrastructure — Use Ours
The JobSearcher API provides programmatic access to the full structured job dataset. Search, filter, and retrieve global job listings at scale. Build job boards, AI copilots, analytics platforms, and research tools on a foundation that's already built and continuously updated.
// Query the global job listings database
GET /api/v1/jobs
?q=machine learning engineer
&location=remote
&posted_after=2026-01-01
&taxonomy=onet:15-2051
&format=json
// Historical labor demand query
GET /api/v1/jobs/history
?title=data scientist
&date_range=2024-01-01:2026-01-01
&granularity=monthly| FREE | OPEN API Broad access to current job listings, structured search, and standard filters. Designed for developers, researchers, and small-scale applications. |
| PRO | SCALE & DEPTH Higher rate limits, historical employment data access, taxonomy-level queries, and priority support for production applications |
| BULK | FULL DATASET ACCESS Complete data exports, streaming updates, and webhook integrations for enterprise-scale workforce analytics, AI training datasets, and embeddings pipelines. |
Job Boards
AI Copilots
Analytics Platforms
Policy & Economics
Workforce Planning
Career Advisors
Recruiting Tech
Research Tools
One Dataset, Many Applications
The same canonical, structured job data serves different users in different ways. JobSearcher is designed as raw material for the ecosystem — not a single product, but a platform that powers many.
AI ASSISTANTS
ANSWERING JOB QUERIES
AI assistants and chatbots use the structured job dataset to answer questions like "What companies are hiring data engineers in Austin?" with real, current, deduplicated data.
RESEARCHERS
LABOR MARKET ANALYSIS
Economists and workforce researchers use the historical job posting dataset for longitudinal labor demand analysis, skill shift studies, and economic modeling.
PRODUCT BUILDERS
JOB SEARCH PRODUCTS
Developers build niche job boards, career tools, and matching engines on top of the job search API — without maintaining their own ingestion and deduplication pipeline.
ENTERPRISES
WORKFORCE ANALYTICS
Companies use employment data from JobSearcher for competitive intelligence, hiring benchmarking, and understanding where talent demand is shifting.
POLICY TEAMS
ECONOMIC MODELING
Government agencies and think tanks use structured labor market data for workforce planning, regional economic analysis, and policy research.
AI COMPANIES
TRAINING & RAG
AI companies use the structured job dataset for fine-tuning models, building embeddings, and powering retrieval-augmented generation systems that understand the labor market.
From data to Understanding
Structured job data is the foundation. JobSearcher is building an intelligence layer that transforms raw listings into labor market insights — not just a dataset, but an evolving model of how the world works.
HIRING TRENDS
Role demand over time. Geographic comparisons. Emerging job categories. A real-time pulse of global labor demand.
SKILL INTELLIGENCE
Skill extraction from descriptions. Demand trends. Adjacency graphs. Understand what the market actually wants.
CAREER PATHWAYS
Role transitions. Important skill gaps. Progression patterns. Data-driven navigation of career trajectories.
COMPANY INTELLIGENCE
Hiring behavior and velocity. Growth signals. Role distribution. Understand how organizations build teams.
Beyond flat records, JobSearcher is building a connected model of the labor market — mapping the relationships between jobs, skills, companies, industries, and geographies into a queryable knowledge graph of work.
JOBS → SKILLS
How are skill requirements shifting over time? Which skills does each role require?
SKILLS → CAREERS
Which skills unlock which career transitions? What are the adjacent roles?
COMPANIES → ROLES
What does each company's hiring profile look like? Where are they growing?
INDUSTRIES → TRENDS
How is hiring behavior shifting across NAICS industries and O*NET occupations?
The Longitudinal Record of Labor Demand
Most job platforms show you what's live right now. JobSearcher preserves the full historical job posting dataset — a time-series of global employment data that enables research, trend analysis, and economic modeling impossible with ephemeral listings alone. This makes JobSearcher not just a search engine but a research-grade archive of how the labor market evolves.
JOBS OVER TIME
Every listing is timestamped and preserved. Query the dataset by any date range to study how hiring demand has shifted.
LONGITUDINAL ANALYSIS
Track the rise and fall of job titles, skills, and industries. Study seasonal patterns, market corrections, and structural shifts in employment data.
VERSION HISTORY
Job descriptions change. Titles shift. Locations update. The archive captures every version, creating a complete revision history of labor market data.
A Better Global Job Search — as a Byproduct of Better Data
When you search JobSearcher, you're querying a deduplicated, structured, global job listings database — not a walled garden of paid placements. Every listing is normalized, comparable, and enriched with metadata you won't find on traditional job platforms.
The result is a cleaner, more comprehensive job search: fewer duplicates, richer detail pages, and a complete view of what's actually available in the labor market.
COMPREHENSIVE RESULTS
Search across sources. See the full market, not a slice of it.
DEDUPLICATED LISTINGS
One canonical listing per role, no matter how many sites carry it.
RICH DETAIL PAGES
Full descriptions, related roles, application pathways, and structured metadata.
AI-ASSISTED DISCOVERY
Explain this job. Am I a fit? Find similar roles. Intelligent search, not just keyword matching.
Raw Material for Innovation
JobSearcher's labor market data is designed to be composed into new products, insights, and systems. Aggregated employment datasets — top companies hiring, emerging roles, regional demand shifts. Embeddings-ready corpora for AI training. Structured exports for workforce analytics. Custom pipelines for enterprise workforce planning. This is the raw material of a structured job dataset, not a finished product — a job listings database that powers whatever you need to build.
Signal Over Noise
Every layer of the job data infrastructure is designed for quality and reliability. Rigorous deduplication across thousands of sources. Consistent taxonomy tagging against O*NET and NAICS standards. Transparent source aggregation. Continuous, near real-time updates ensure the structured job dataset reflects the current state of the global labor market — not yesterday's snapshot. And on the roadmap: job authenticity scoring, freshness indicators, and ghost job detection.
DEDUPLICATED
Canonical records
STRUCTURED
Consistent schema
CLASSIFIED
O*NET & NAICS
FRESH
Near real-time
VERSIONED
Change tracking
Mapping the World of Work, Everywhere
International coverage with English-normalized data. Cross-market comparability for labor market analysis across regions. Remote and location-based roles treated as first-class data dimensions. Whether you're analyzing employment data in Berlin, querying the job search API for remote engineering roles, or studying longitudinal labor demand across Asia-Pacific — you're querying the same structured, global job listings database.
JOBSEARCHER
OUR VISION
We're Building the Canonical Dataset of Global Labor Demand
JobSearcher is the foundational data layer for the future of job search, labor economics, workforce planning, and AI-powered career navigation. A structured, continuously updated, global dataset of job postings — accessible via API and designed for AI systems, developers, researchers, and the labor market itself.
Better data leads to better matching, better policy decisions, and better outcomes for everyone in the labor market. That means a single parent in Ohio finds the right role faster. A policymaker in Brussels spots a workforce gap before it becomes a crisis. An AI assistant gives a career-changer genuinely useful guidance instead of stale links.
Jobs are data. We structure that data.