Collectors · Jobs

LinkedIn jobs API in Python

Public LinkedIn job listings for a role and location. One POST from Python with query, rows back as JSON with 19 fields, $0.001 per delivered job. No proxies or parsers to maintain.

Get an API key See the collector

A Python requests snippet that posts { — A Python requests call to the linkedin_jobs collector returns one dict per job, at $0.001 each.

First call

Install, call, read the rows.

pip install requests

The only dependency.

import os, requests

r = requests.post(
    "https://api.quanticdata.io/v1/scraper/collectors/linkedin_jobs/run",
    headers={"Authorization": f"Bearer {os.environ['QD_API_KEY']}"},
    json={
        "query": "data engineer",
        "location": "United States",
        "max_results": 50,
    },
    timeout=120,
)
r.raise_for_status()
run = r.json()["payload"]
print(run["count"], "rows,", run["cost"]["usd"], "USD")
for row in run["results"]:
    print(row.get("title"), row.get("company"), row.get("company_url"), row.get("location"))

Python: one LinkedIn jobs run (Data engineer (US)), printing title, company, company_url, location

The key comes from an environment variable, never from the code. The run answers in the same request when it finishes in time; payload.results holds the rows and payload.cost what they cost.

Request parameters

What the collector takes.

Input of the LinkedIn jobs collector
Parameter	Type	Required	Meaning
`query`	string	yes	Job title or keywords, e.g. "data engineer".
`location`	string	no	City, region or country, e.g. "New York" or "Italy".
`remote_only`	boolean	no (default false)	Only listings LinkedIn marks as remote.
`posted_within_days`	integer	no	Only listings posted in the last N days (1–30).
`country`	string	no	ISO 3166-1 alpha-2 code — proxy exit geo and Google locale (gl). Omit for the default pool.
`include_details`	boolean	no (default false)	Also read each job's public posting: full description, seniority level, employment type, job function, industries and applicant count. One extra request per job, so runs take longer; off by default.
`max_results`	integer	no (default 25)	How many jobs to deliver at most (1–300). You pay only for delivered jobs.

Response fields

19 fields per job.

Fields of each row (job)
Field	Type	What it holds
`rank`	integer	1-based position across pages.
`title`	string	Job title.
`company`	string, nullable	Employer.
`company_url`	string, nullable	Employer's LinkedIn page.
`location`	string, nullable	Location as shown.
`posted_at`	string, nullable	Posting date (ISO, from the card's <time>).
`posted_label`	string, nullable	Posting age as shown ("2 weeks ago").
`salary`	string, nullable	Salary chip when LinkedIn shows one.
`benefits`	string, nullable	Benefits chip when shown.
`job_id`	string, nullable	LinkedIn job id (dedupe key).
`link`	string, nullable	Listing URL.
`logo`	string, nullable	Company logo URL.
`description`	string, nullable	With include_details: the full posting text.
`seniority`	string, nullable	With include_details: seniority level (Entry level, Mid-Senior level…).
`employment_type`	string, nullable	With include_details: Full-time, Part-time, Contract…
`job_function`	string, nullable	With include_details: job function as LinkedIn classifies it.
`industries`	string[], nullable	With include_details: the employer's industries.
`applicants`	integer, nullable	With include_details: applicant count as a number (200 for "Over 200 applicants").
`applicants_label`	string, nullable	With include_details: applicant count as shown.

Add ?format=csv to the runs endpoint to get the same rows as CSV.

Long runs

Run in the background, then poll.

A run that needs more time can go to the background: send "async": true, get 202 with the run id, and poll the runs endpoint until the status is done. Reading a run is free.

import os, time, requests

H = {"Authorization": f"Bearer {os.environ['QD_API_KEY']}"}
r = requests.post("https://api.quanticdata.io/v1/scraper/collectors/linkedin_jobs/run", headers=H,
                  json={**{"query":"data engineer","location":"United States","max_results":50}, "async": True}, timeout=30)
run_id = r.json()["payload"]["run_id"]
while True:
    run = requests.get(f"https://api.quanticdata.io/v1/scraper/collectors/runs/{run_id}", headers=H, timeout=30).json()["payload"]
    if run["status"] in ("done", "failed"):
        break
    time.sleep(5)
print(run["status"], run["count"])

Python: background run and polling

Errors

What can come back, and what is billed.

Collector run errors
Status	Meaning	Billed
`400`	The input does not match the collector (`payload.errors`)	No
`401`	Missing or wrong API key	No
`402`	Balance and free credit do not cover the run: top up	No
`422`	The input can never resolve; retrying the same input cannot help	No
`424`	The run failed at the source (blocked, timed out, layout changed); safe to retry	No
`429`	Rate limit or a per-account brake; wait for `Retry-After`	No

Only delivered rows are billed: a run that delivers nothing costs nothing.

Price

$0.001 per delivered job.

What LinkedIn jobs rows cost at list price
Rows	Cost
1	$0.001
1,000	$1.00
10,000	$10.00

Up to 300 jobs per run; split larger jobs into several runs. The $2 free credit every month covers about 2,000 jobs. No subscription; paid tiers lower the unit price. All collector prices: collector pricing.

Before you run it

Questions developers ask

Is there a free LinkedIn jobs API?

Every account gets $2 of free usage each month, about 2,000 jobs of this collector at $0.001 each, with no card.

Do I have to handle proxies, retries or blocks?

No. The collector runs on our network and handles them; a run blocked at the source returns 424 and is not billed.

How many rows can one run return?

Up to 300 jobs per run (the max_results input, where the collector has one).

Can an AI agent run it?

Yes: the MCP server exposes it as the run_collector tool, with the same input and price. See the MCP server.

Run it on the free credit.

Create an API key, export it as QD_API_KEY and run the example. $0.001 per delivered job, no subscription.

Get an API key LinkedIn jobs API