linkedin-jobs-scraper

> Scrape public available jobs on Linkedin using headless browser. > For each job, the following fields are extracted: > job_id, > link, > apply_link, > title, > company, > company_link, > company_img_link, > place, > description, > description_html, > date, > insights. > > It's also available an equivalent npm package.

⚠ DISCLAIMER This package is meant for personal or educational use only. All the data extracted by using this package is publicly available on the LinkedIn website and it remains owned by LinkedIn company. I am not responsible in any way for the inappropriate use of data extracted through this library.

Table of Contents

• Requirements
• Installation
• Usage
• Anonymous vs authenticated session
• Rate limiting
• Proxy mode
• Filters
• Company filter
• Logging
• Sponsors
• License

Requirements

• Chrome or Chromium
• Chromedriver: latest version tested is 125.0.6422.141 (Dockerfile)
• Python >= 3.7

Installation

Install package:

pip install linkedin-jobs-scraper

Usage

import logging
from linkedin_jobs_scraper import LinkedinScraper
from linkedin_jobs_scraper.events import Events, EventData, EventMetrics
from linkedin_jobs_scraper.query import Query, QueryOptions, QueryFilters
from linkedin_jobs_scraper.filters import RelevanceFilters, TimeFilters, TypeFilters, ExperienceLevelFilters, \
    OnSiteOrRemoteFilters, SalaryBaseFilters

# Change root logger level (default is WARN)
logging.basicConfig(level=logging.INFO)


# Fired once for each successfully processed job
def on_data(data: EventData):
    print('[ON_DATA]', data.title, data.company, data.company_link, data.date, data.link, data.insights,
          len(data.description))


# Fired once for each page (25 jobs)
def on_metrics(metrics: EventMetrics):
    print('[ON_METRICS]', str(metrics))


def on_error(error):
    print('[ON_ERROR]', error)


def on_end():
    print('[ON_END]')


scraper = LinkedinScraper(
    chrome_executable_path=None,  # Custom Chrome executable path (e.g. /foo/bar/bin/chromedriver)
    chrome_binary_location=None,  # Custom path to Chrome/Chromium binary (e.g. /foo/bar/chrome-mac/Chromium.app/Contents/MacOS/Chromium)
    chrome_options=None,  # Custom Chrome options here
    headless=True,  # Overrides headless mode only if chrome_options is None
    max_workers=1,  # How many threads will be spawned to run queries concurrently (one Chrome driver for each thread)
    slow_mo=0.5,  # Slow down the scraper to avoid 'Too many requests 429' errors (in seconds)
    page_load_timeout=40  # Page load timeout (in seconds)    
)

# Add event listeners
scraper.on(Events.DATA, on_data)
scraper.on(Events.ERROR, on_error)
scraper.on(Events.END, on_end)

queries = [
    Query(
        options=QueryOptions(
            limit=27  # Limit the number of jobs to scrape.            
        )
    ),
    Query(
        query='Engineer',
        options=QueryOptions(
            locations=['United States', 'Europe'],
            apply_link=True,  # Try to extract apply link (easy applies are skipped). If set to True, scraping is slower because an additional page must be navigated. Default to False.
            skip_promoted_jobs=True,  # Skip promoted jobs. Default to False.
            page_offset=2,  # How many pages to skip
            limit=5,
            filters=QueryFilters(
                company_jobs_url='https://www.linkedin.com/jobs/search/?f_C=1441%2C17876832%2C791962%2C2374003%2C18950635%2C16140%2C10440912&geoId=92000000',  # Filter by companies.                
                relevance=RelevanceFilters.RECENT,
                time=TimeFilters.MONTH,
                type=[TypeFilters.FULL_TIME, TypeFilters.INTERNSHIP],
                on_site_or_remote=[OnSiteOrRemoteFilters.REMOTE],
                experience=[ExperienceLevelFilters.MID_SENIOR],
                base_salary=SalaryBaseFilters.SALARY_100K
            )
        )
    ),
]

scraper.run(queries)

Anonymous vs authenticated session

⚠ WARNING: due to lack of time, anonymous session strategy is no longer maintained. If someone wants to keep support for this feature and become a project maintainer, please be free to pm me.

By default the scraper will run in anonymous mode (no authentication required). In some environments (e.g. AWS or Heroku) this may be not possible though. You may face the following error message:

Scraper failed to run in anonymous mode, authentication may be necessary for this environment.

In that case the only option available is to run using an authenticated session. These are the steps required:

1. Login to LinkedIn using an account of your choice.
2. Open Chrome developer tools:

3. Go to tab Application, then from left panel select Storage -> Cookies -> https://www.linkedin.com. In the main view locate row with name li_at and copy content from the column Value.

4. Set the environment variable LI_AT_COOKIE with the value obtained in step 3, then run your application as normal. Example:

LI_AT_COOKIE= python your_app.py

Rate limiting

You may experience failing requests with the status code 429. This means you are sending too many request to the server and they are being throttled. You can overcome this by:

• Trying a higher value for slow_mo parameter (this will slow down scraper execution).
• Reducing the value of max_workers to limit concurrency. I recommend to use no more than one worker in authenticated mode.

The right value for slow_mo parameter largely depends on rate-limiting settings on Linkedin servers (and this can vary over time). For the time being, I suggest a value of at least 1.3 in anonymous mode and 0.5 in authenticated mode.

Filters

It is possible to customize queries with the following filters:

• RELEVANCE:
  • RELEVANT
  • RECENT
• TIME:
  • DAY
  • WEEK
  • MONTH
  • ANY
• TYPE:
  • FULL_TIME
  • PART_TIME
  • TEMPORARY
  • CONTRACT
• EXPERIENCE LEVEL:
  • INTERNSHIP
  • ENTRY_LEVEL
  • ASSOCIATE
  • MID_SENIOR
  • DIRECTOR
• ON SITE OR REMOTE:
  • ON_SITE
  • REMOTE
  • HYBRID
• INDUSTRY:
  • AIRLINES_AVIATION
  • BANKING
  • CIVIL_ENGINEERING
  • COMPUTER_GAMES
  • ENVIRONMENTAL_SERVICES
  • ELECTRONIC_MANUFACTURING
  • FINANCIAL_SERVICES
  • INFORMATION_SERVICES
  • INVESTMENT_BANKING
  • INVESTMENT_MANAGEMENT
  • IT_SERVICES
  • LEGAL_SERVICES
  • MOTOR_VEHICLES
  • OIL_GAS
  • SOFTWARE_DEVELOPMENT
  • STAFFING_RECRUITING
  • TECHNOLOGY_INTERNET
• BASE SALARY:
  • SALARY_40K
  • SALARY_60K
  • SALARY_80K
  • SALARY_100K
  • SALARY_120K
  • SALARY_140K
  • SALARY_160K
  • SALARY_180K
  • SALARY_200K
• COMPANY:
  • See below

See the following example for more details:

from linkedin_jobs_scraper.query import Query, QueryOptions, QueryFilters
from linkedin_jobs_scraper.filters import RelevanceFilters, TimeFilters, TypeFilters, ExperienceLevelFilters, \
    OnSiteOrRemoteFilters, IndustryFilters, SalaryBaseFilters
query = Query(
    query='Engineer',
    options=QueryOptions(
        locations=['United States'],        
        apply_link=True,
        skip_promoted_jobs=True,
        limit=5,
        filters=QueryFilters(
            relevance=RelevanceFilters.RECENT,
            time=TimeFilters.MONTH,
            type=[TypeFilters.FULL_TIME, TypeFilters.INTERNSHIP],
            experience=[ExperienceLevelFilters.INTERNSHIP, ExperienceLevelFilters.MID_SENIOR],
            on_site_or_remote=[OnSiteOrRemoteFilters.REMOTE],
            industry=[IndustryFilters.IT_SERVICES],
            base_salary=SalaryBaseFilters.SALARY_100K
        )
    )
)

Industry Filter

You will probably need to add the industry filter to the IndustryFilters class in filters.py

To find the numeric code for the industry:

1. Perform the search on LinkedIn in a browser, with the industry filter applied.
2. The numeric code is in the URL, immediately after f_I . For example URL https://www.linkedin.com/jobs/search/?currentJobId=3661007408&distance=25&f_E=3%2C4&f_I=43%2C46%2C41%2C45&f_JT=F%2CC&geoId=102257491&keywords=Product Owner&refresh=true contains text f_I=43%2C46%2C41%2C45 indicating a filter is applied on industry codes 43, 46, 41 and 45.

Company Filter

It is also possible to filter by company using the public company jobs url on LinkedIn. To find this url you have to:

1. Login to LinkedIn using an account of your choice.
2. Go to the LinkedIn page of the company you are interested in (e.g. https://www.linkedin.com/company/google).
3. Click on jobs from the left menu.

4. Scroll down and locate See all jobs or See jobs button.

5. Right click and copy link address (or navigate the link and copy it from the address bar).
6. Paste the link address in code as follows:

query = Query(    
    options=QueryOptions(        
        filters=QueryFilters(
            # Paste link below
            company_jobs_url='https://www.linkedin.com/jobs/search/?f_C=1441%2C17876832%2C791962%2C2374003%2C18950635%2C16140%2C10440912&geoId=92000000',        
        )
    )
)

Logging

Package logger can be retrieved using namespace li:scraper. Default level is INFO. It is possible to change logger level using environment variable LOG_LEVEL or in code:

import logging

# Change root logger level (default is WARN)
logging.basicConfig(level = logging.DEBUG)

# Change package logger level
logging.getLogger('li:scraper').setLevel(logging.DEBUG)

# Optional: change level to other loggers
logging.getLogger('urllib3').setLevel(logging.WARN)
logging.getLogger('selenium').setLevel(logging.WARN)

Sponsors

Proxycurl APIs

Scrape public LinkedIn profile data at scale with Proxycurl APIs.

• Scraping Public profiles are battle tested in court in HiQ VS LinkedIn case.
• GDPR, CCPA, SOC2 compliant.
• High rate Limit - 300 requests/minute Fast APIs respond in ~2s.
• Fresh data - 88% of data is scraped real-time, other 12% are not older than 29 days.
• High accuracy.
• Tons of data points returned per profile.

Built for developers, by developers.

License

MIT License

If you like the project and want to contribute you can donate something here!