Glassdoor Job Scraper

🔍 What is Glassdoor Job Scraper?

Glassdoor Job Scraper extracts structured job listings from glassdoor.com — with company metadata. The input is built around keyword search, location filters, and controllable result limits, so you can rerun the same search universe consistently over time.

glassdoor.com is a public source platform, but it does not provide the kind of structured export most teams need for recurring data workflows. This actor bridges that gap by turning the source into clean JSON with company metadata, with repeatable source access and a schema that is easier to reuse in dashboards, enrichment pipelines, and agent workflows.

🎯 What you can do with this actor

• Build richer employer datasets with company profiles, ratings, social links, and career-page signals where the source exposes them.
• Feed compact listing data into AI agents, MCP tools, and ranking workflows without carrying full raw payloads every time.
• Start with lightweight search runs, then enable detail enrichment only when you need deeper company or listing context.

✨ Why choose this actor?

Feature	This actor	Typical alternatives
Enrichment depth	Structured seller / company signals where the source exposes them	Often limited to title, price, and URL
Collection strategy	Can stay lightweight or add enrichment only when needed	Often fixed to one scraping mode
AI-agent usability	Compact output mode for smaller, more controllable payloads	Often full payload only
Schema quality	Keeps company metadata in a consistent output shape	Often inconsistent across runs

🚀 Quick start

Basic search:

{
  "query": "software engineer",
  "country": "US",
  "maxResults": 50,
  "maxPages": 5,
  "sort": "relevance",
  "includeDetails": true,
  "includeCompanyProfile": false,
  "compactMode": false,
  "descriptionMaxLength": 0,
  "incrementalMode": false,
  "emitUnchanged": false,
  "emitExpired": false
}

With enrichment:

{
  "query": "software engineer",
  "country": "US",
  "maxResults": 50,
  "maxPages": 5,
  "sort": "relevance",
  "includeDetails": true,
  "includeCompanyProfile": true,
  "compactMode": false,
  "descriptionMaxLength": 0,
  "incrementalMode": false,
  "emitUnchanged": false,
  "emitExpired": false
}

Incremental monitoring:

{
  "query": "software engineer",
  "country": "US",
  "maxResults": 50,
  "maxPages": 5,
  "sort": "relevance",
  "includeDetails": true,
  "includeCompanyProfile": false,
  "compactMode": false,
  "descriptionMaxLength": 0,
  "incrementalMode": true,
  "emitUnchanged": false,
  "emitExpired": false,
  "stateKey": "daily-monitor"
}

📊 Sample output

{
  "title": "Example title",
  "company": "Example company",
  "location": "Example location",
  "url": "https://glassdoor.com"
}

⚙️ Input reference

Parameter	Type	Default	Description
Search
query	string	—	Job search keywords. Use JSON array for multi-query.
country	enum	"US"	Which Glassdoor domain to search (21 markets).
location	string	—	City, state, or region. Use JSON array for multi-location.
startUrls	array	—	Direct Glassdoor search or job detail URLs.
maxResults	integer	50	Maximum total job listings to return.
maxPages	integer	5	Maximum SERP pages to scrape per search source.
Filters
postedDays	integer	—	Only return jobs posted within this many days. Snapped to nearest valid: 1, 3, 7, or 14.
jobType	enum	—	Filter by employment type.
remoteFilter	enum	—	Filter by remote work arrangement.
radius	integer	—	Radius around the location in miles/km (5, 10, 15, 25, 35, 50, or 100). Snapped to nearest valid value.
sort	enum	"relevance"	Sort results by relevance or posting date.
Enrichment
includeDetails	boolean	true	Fetch each job's detail page for full description, salary data, and company info.
includeCompanyProfile	boolean	false	Fetch company overview pages for industry, headcount, and more.
Output Format
compactMode	boolean	false	Output only 12 core fields (jobId, title, company, location, salary, dates, URL). Ideal for AI agents and MCP workflows to reduce token usage.
descriptionMaxLength	integer	0	Truncate job descriptions to this many characters. Set to 0 for full descriptions. Useful for reducing output size in AI/LLM pipelines.
Incremental Tracking
incrementalMode	boolean	false	Compare against previous run state. Requires stateKey.
stateKey	string	—	Stable identifier for the tracked search universe (e.g. "us-software-nyc").
emitUnchanged	boolean	false	When incremental, also emit records that haven't changed.
emitExpired	boolean	false	When incremental, also emit records no longer found.

📦 Output fields

Each result can include company metadata, depending on listing content and the enrichment options enabled for the run.

This actor returns structured dataset items through the default Apify dataset output. See the sample output above for the practical field shape.

⚠️ Known limitations

• Contact information is only returned when the source exposes it directly; many listings will still rely on apply URLs rather than named contacts.
• Field population rates always depend on the source site itself, so null values are normal for data points the source does not publish on every listing.

💰 How much does it cost to scrape glassdoor job scraper?

This actor uses pay-per-event pricing, so you pay a small run-start fee and then only for results that are actually emitted.

Event	Price	When
actor-start	$0.01	Each run
result	$0.002	Per emitted record

Example costs:

Scenario	Results	Cost
Quick test	10	$0.03
Daily monitor	50	$0.11
Full scrape	500	$1.01

💡 Use cases

Recruiting and sourcing

Pull glassdoor.com job listings into dashboards, triage queues, or recruiter workflows without re-normalizing the source on every run.

Recurring monitoring

Track only newly posted or changed listings on scheduled runs, which is better suited to alerts and daily pipeline jobs than repeated full exports.

Outreach and hiring-intent research

Use employer, contact, and apply fields to support account research, outreach queues, or company watchlists when the source provides those details.

AI-agent and MCP workflows

Feed compact listing data into ranking, summarization, classification, or agent pipelines without burning unnecessary context on large descriptions.

🤖 AI-agent and MCP usage

This actor is suitable for AI-agent workflows because the output is structured and the input can intentionally reduce payload size for downstream tools.

• descriptionMaxLength lets you cap description size so larger batches stay practical in model context windows.

{
  "query": "software engineer",
  "country": "US",
  "maxResults": 10,
  "maxPages": 5,
  "sort": "relevance",
  "includeDetails": true,
  "includeCompanyProfile": false,
  "compactMode": false,
  "descriptionMaxLength": 300,
  "incrementalMode": false,
  "emitUnchanged": false,
  "emitExpired": false
}

🔄 Incremental mode

Incremental mode is intended for repeated monitoring runs where only new or changed listings should be emitted.

Change type	Meaning
NEW	First time seen in the monitored result set
CHANGED	Previously seen listing with updated content
UNCHANGED	Same listing and content as a prior run when unchanged emission is enabled
EXPIRED	Listing disappeared from the monitored result set when expired emission is enabled

📖 How to scrape glassdoor job scraper

1. Open the actor in Apify Console and review the input schema.
2. Enter your search query and location settings, then set maxResults for the amount of data you need.
3. Enable optional enrichment fields only when you need richer output such as descriptions, contacts, or company data.
4. Run the actor and export the dataset as JSON, CSV, or Excel for downstream analysis.

❓ FAQ

What data does this actor return from glassdoor.com?

It returns structured listing records with fields such as company metadata, plus the core identifiers and metadata defined in the dataset schema.

Can I fetch full descriptions and detail fields?

Yes. Enable the detail-related input options when you need richer fields such as descriptions, employer metadata, or contact details from the listing detail pages.

Does it support recurring monitoring?

Yes. Incremental mode is built for recurring runs where you only want newly seen or changed listings instead of a full repeat dataset every time.

Is it suitable for AI agents or MCP workflows?

Yes. Compact mode and output-size controls make it easier to use the actor in AI-agent workflows where predictable fields matter more than raw page size.

Why use this actor instead of scraping the site ad hoc?

Because it already handles repeatable source access, keeps a stable schema, and exposes filters and enrichment options in a form that is easier to automate repeatedly.

Is scraping glassdoor.com legal?

This actor is intended for publicly accessible data workflows. Always review the target site terms and your own legal requirements for the way you plan to use the data.

🔗 Related actors

• Arbeitsagentur Jobs Feed — German Federal Employment Agency — Alternative structured job-feed workflow
• Company Jobs Tracker — Alternative structured job-feed workflow
• Dice.com Job Scraper — Alternative structured job-feed workflow
• Duunitori Scraper — Alternative structured job-feed workflow
• Indeed Job Scraper — Alternative structured job-feed workflow