Internshala Internship & Jobs Scraper

🎓 Internshala Internship & Jobs Scraper – Stipend, Skills & Hiring Data Extraction

The Internshala Internship & Jobs Scraper extracts structured internship and job listings from Internshala, India's largest student hiring platform, including stipend ranges, required skills, perks, duration, application deadlines, and the number of openings per role. Point it at internships or jobs, apply filters for category, city, remote work, part-time status, or minimum stipend, and it returns clean records ready for recruitment analytics, salary benchmarking, or a job aggregator feed.

Internshala sits at the entry-level end of the Indian hiring market, which makes it an unusually good source of early-career labour market data. Where general job boards skew towards experienced roles, this platform concentrates internship listings, fresher jobs, and work-from-home opportunities aimed at students and recent graduates — a segment that is hard to measure anywhere else.

The Internshala scraper covers both the search listing pages and, optionally, each listing's detail page, where the long-form description, skill requirements, and perk list live. It runs without a headless browser, which keeps runs efficient even across thousands of listings, and proxy rotation is handled automatically inside the Actor.

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📊 What Data Can You Extract with This Internshala Jobs Scraper?

Each dataset record represents one internship or job listing, with 26 fields grouped as follows.

Category	Fields	What it gives you
🆔 Listing identity	listingId, listingType, url, title, category, scrapedAt	Stable identifiers, the canonical listing URL, role title, profile category, and the capture timestamp
🏢 Employer detail	company, companyUrl	Hiring company name and its Internshala company page link
📍 Location & work mode	location, isRemote, isPartTime	City or work-from-home designation plus explicit remote and part-time flags
💰 Compensation	stipend, stipendMin, stipendMax, isUnpaid	Raw stipend text alongside parsed numeric minimum and maximum, and an unpaid flag
📅 Timing & scale	duration, startDate, applyBy, openings, postedAt	Internship length, start date, application deadline, headcount, and posting recency
🧠 Requirements & benefits	skills, perks, description, hasJobOffer	Skill array, perk array, full description text, and whether a pre-placement offer is on the table
🔥 Hiring signals	isActivelyHiring, isEarlyApplicant	Platform badges indicating responsive employers and low-competition listings

The standout fields are stipendMin and stipendMax. Internshala displays stipends as free text — "₹10,000 - 15,000 /month", "Unpaid", "Performance based" — which is useless for analysis until parsed. The Internshala scraper returns both the original stipend string and the parsed numeric bounds, so you can compute medians, plot distributions by city, and rank categories by pay without writing your own parser.

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🌟 Key Features of the Internshala Jobs Scraper

Feature	Description
🔀 Internships or jobs	The listingType selector switches the scraper between Internshala's internship and job sections
💰 Parsed stipend ranges	stipendMin and stipendMax convert free-text stipend strings into numeric INR values for analysis
🧠 Skills and perks arrays	Detail-page scraping returns the full required-skills list and the perks offered with each listing
🏠 Remote and part-time filters	workFromHome and partTime restrict the crawl to remote-only or part-time listings at source
🎯 Category and city targeting	categories and cities narrow the crawl to specific profiles and locations instead of the whole board
📄 Optional detail-page crawl	scrapeDetails toggles between fast listing-level extraction and full description, skill, and perk enrichment
⚙️ Throughput controls	maxListings, maxPages, requestDelay, and maxConcurrency let you trade speed against politeness
🔥 Hiring intent signals	isActivelyHiring and isEarlyApplicant surface which employers respond and which listings are uncontested
🛡️ Automatic proxy rotation	Proxy handling is built into the Actor — there is nothing to configure

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🚀 Why Choose This Internshala Jobs Scraper?

Stipend data arrives analysis-ready. Most job scrapers hand you a compensation string and leave the parsing to you. Returning stipendMin, stipendMax, and isUnpaid alongside the raw stipend text means salary benchmarking works the moment the run finishes.

Filtering happens before the crawl, not after. Setting categories, cities, workFromHome, partTime, and minStipend narrows what gets fetched in the first place. That is faster and cheaper than pulling the entire board and discarding 95% of it downstream.

Detail-page enrichment is optional and controllable. With scrapeDetails enabled you get description, skills, and perks for every listing; with it disabled you get a fast listing-level sweep. The choice is yours per run rather than baked into the Actor.

Built for repeatable monitoring. listingId, postedAt, and scrapedAt together let you deduplicate across runs, detect newly posted roles, and build a genuine time series of the Indian entry-level hiring market rather than a series of disconnected snapshots.

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📥 Input

{
  "listingType": "internships",
  "categories": [],
  "cities": [],
  "workFromHome": false,
  "partTime": false,
  "minStipend": 0,
  "maxListings": 200,
  "maxPages": 20,
  "scrapeDetails": true,
  "requestDelay": 500,
  "maxConcurrency": 3
}

🔧 Internshala Scraper Input Fields

Field	Type	Required	Default	Description
listingType	string	✅ Yes	internships	Choose whether to scrape internships or jobs. Options: internships, jobs
categories	array	No	[]	Filter by specific categories/profiles
cities	array	No	[]	Filter by specific cities
workFromHome	boolean	No	false	Only scrape work-from-home/remote listings
partTime	boolean	No	false	Only scrape part-time listings
minStipend	integer	No	0	Filter by minimum stipend/salary value (in INR)
maxListings	integer	No	200	Maximum number of listings to scrape (0 = unlimited)
maxPages	integer	No	20	Maximum number of search pages to crawl per filter (0 = unlimited)
scrapeDetails	boolean	No	true	Navigate to detail page of each listing to scrape description, perks, skills, etc.
requestDelay	integer	No	500	Delay between requests in milliseconds
maxConcurrency	integer	No	3	Maximum number of concurrent requests

💡 Input Examples

Remote software internships paying at least ₹10,000 per month

{
  "listingType": "internships",
  "categories": ["Software Development"],
  "workFromHome": true,
  "minStipend": 10000,
  "maxListings": 500,
  "scrapeDetails": true
}

Fast city-level jobs sweep without detail pages

{
  "listingType": "jobs",
  "cities": ["Bangalore", "Hyderabad", "Pune"],
  "scrapeDetails": false,
  "maxListings": 1000,
  "maxPages": 40
}

Part-time internship monitoring with gentle crawl settings

{
  "listingType": "internships",
  "partTime": true,
  "maxListings": 200,
  "requestDelay": 1200,
  "maxConcurrency": 2
}

---

📤 Output

{
  "listingId": "3236762",
  "listingType": "internships",
  "url": "https://internshala.com/internship/detail/work-from-home-software-development-internship-at-queens-of-change-foundation",
  "title": "Software Development",
  "company": "Queens Of Change Foundation",
  "companyUrl": null,
  "location": "Work from home",
  "isRemote": true,
  "stipend": "Unpaid",
  "stipendMin": 0,
  "stipendMax": 0,
  "duration": "1 Month",
  "startDate": "immediately",
  "applyBy": "9 Sep' 26",
  "openings": 20,
  "skills": [".NET", "C++ Programming", "CSS", "English Proficiency (Spoken)", "HTML", "Java", "JavaScript"],
  "perks": ["Certificate", "Letter of recommendation", "Flexible work hours", "Job offer"],
  "isPartTime": true,
  "hasJobOffer": true,
  "isUnpaid": true,
  "isActivelyHiring": true
}

🧾 Internshala Listing Output Fields

Field	Type	Description
listingId	string | null	Identifier of the listing
listingType	string | null	Whether the record is an internship or a job
url	string | null	Canonical URL of the scraped listing
title	string | null	Role or profile title
company	string | null	Hiring company name
companyUrl	string | null	URL of the listing's company page
location	string | null	Location of the listing
isRemote	boolean | null	Whether the listing is work-from-home
stipend	string | null	Stipend as displayed on the listing
stipendMin	integer | null	Parsed lower bound of the stipend
stipendMax	integer | null	Parsed upper bound of the stipend
duration	string | null	Duration of the internship or role
startDate	string | null	Start date of the position
applyBy	string | null	Application deadline
openings	integer | null	Number of openings advertised
skills	array | null	Skills required or listed
perks	array | null	Perks offered with the listing
description	string | null	Long-form description text
isPartTime	boolean | null	Whether the listing is part-time
hasJobOffer	boolean | null	Whether a job offer is attached to the internship
isUnpaid	boolean | null	Whether the listing is unpaid
isActivelyHiring	boolean | null	Whether the employer is flagged as actively hiring
isEarlyApplicant	boolean | null	Whether applying now counts as an early application
postedAt	string | null	When the listing was posted
category	string | null	Category or profile of the listing
scrapedAt	string | null	When this record was scraped

description, skills, and perks are populated by the detail-page crawl. If you set scrapeDetails to false, expect those fields to be sparse or empty.

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💻 How to Use the Internshala Jobs Scraper (Step by Step)

Step 1: Choose between internships and jobs

Open the Actor and set listingType. Internshala separates internships from full-time jobs, and the two sections have different audiences and different compensation semantics — internships report a monthly stipend, jobs report an annual salary band. Decide which market you are studying before anything else, and run two separate jobs if you need both.

Step 2: Narrow the crawl with categories and cities

Use categories to restrict the Internshala scraper to specific profiles such as software development, marketing, or design, and cities to focus on particular metros. Leaving both empty crawls across the board, which is fine for a broad market survey but wasteful when you only care about one segment. Each filter combination is crawled up to maxPages deep.

Step 3: Apply work-mode and stipend filters

Set workFromHome to true for remote-only listings and partTime to true for part-time roles. minStipend filters by the minimum stipend in INR, which is the fastest way to strip unpaid and token-stipend listings out of a compensation study. All three filters apply at the search level, so they reduce crawl volume rather than just trimming output.

Step 4: Decide whether to scrape detail pages

scrapeDetails is the single biggest lever on run cost and data richness. Leave it true when you need description, skills, and perks — essential for skills-demand analysis or for populating a job board. Set it false when you only need titles, companies, locations, and stipends, and you want a fast sweep of a large number of listings.

Step 5: Tune limits, delay, and concurrency

maxListings caps the total number of records and maxPages caps how deep each filter combination is crawled; both accept 0 for unlimited. requestDelay and maxConcurrency control crawl pressure. The defaults — 500 ms between requests, three concurrent requests — are a reasonable balance. Raise the delay and lower concurrency if you see failures on very large runs.

Step 6: Run the Internshala scraper and review results

Press Start and watch the log as pages are crawled and listings extracted. When the run completes, open the Dataset tab. Check that stipendMin and stipendMax are populated for paid roles, and that skills and perks arrays are present if you enabled detail scraping. Spot-check a couple of url values against the live site.

Step 7: Export, deduplicate, and schedule

Export as JSON, CSV, Excel, XML, or HTML, or read the dataset through the API. Deduplicate on listingId when combining runs, and use postedAt to isolate newly published roles. Scheduling the Internshala jobs scraper daily or weekly turns one-off extraction into a hiring-trend dataset that shows which categories, cities, and skills are gaining or losing demand.

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🔌 API Access & Integrations

Run the Internshala scraper synchronously and receive dataset items directly:

curl -X POST "https://api.apify.com/v2/acts/scrapers-hub~internshala-jobs-scraper/run-sync-get-dataset-items?token=YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "listingType": "internships",
    "workFromHome": true,
    "minStipend": 10000,
    "maxListings": 300,
    "scrapeDetails": true
  }'

Compute median stipends by category with the Python client:

import statistics
from collections import defaultdict
from apify_client import ApifyClient

client = ApifyClient("YOUR_TOKEN")

run = client.actor("scrapers-hub/internshala-jobs-scraper").call(run_input={
    "listingType": "internships",
    "cities": ["Bangalore"],
    "maxListings": 500,
    "scrapeDetails": True,
})

by_category = defaultdict(list)
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    if item.get("isUnpaid"):
        continue
    low, high = item.get("stipendMin"), item.get("stipendMax")
    if low and high:
        by_category[item.get("category") or "uncategorised"].append((low + high) / 2)

for category, values in sorted(by_category.items()):
    print(f"{category}: median ₹{statistics.median(values):,.0f}/month across {len(values)} listings")

The Actor also connects to Zapier, Make, Google Sheets, and Slack, and supports webhooks that fire on run completion so new listings can flow into your ATS or alerting channel automatically.

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💡 Best Use Cases for Internshala Internship and Jobs Data

💰 Entry-level stipend benchmarking

Aggregate stipendMin and stipendMax across category and location to establish what internships in each profile and city actually pay. Because isUnpaid isolates the zero-stipend listings, you can report both the share of unpaid roles and the median stipend among paid ones — two numbers that tell very different stories about a segment.

🧠 Skills demand tracking for curriculum design

The skills array is a direct read on what employers are asking entry-level candidates to know. Counting skill frequency over time, segmented by category, shows which tools and languages are rising and which are fading — exactly the evidence training providers and university departments need to justify curriculum changes.

🎯 Recruitment competitive intelligence

Monitor company, openings, stipend, and perks across your category to see how rival employers are positioning their internship programmes. If competitors are offering higher stipends, more openings, or richer perk lists, isActivelyHiring tells you which of them are converting that offer into actual hiring momentum.

📰 Job board and aggregator feeds

Populate a niche job site with listings filtered by categories, cities, and workFromHome. The combination of url, title, company, applyBy, and description gives you everything a listing page needs, while listingId provides the deduplication key and applyBy lets you expire stale entries automatically.

🏠 Remote work share analysis

The isRemote flag, combined with location and postedAt, quantifies how much of India's entry-level hiring is genuinely work-from-home and how that share moves over time. Segmenting by category reveals which functions have normalised remote internships and which still require presence.

🎓 Student career guidance and placement support

Placement cells can filter for hasJobOffer to find internships with pre-placement offers attached, isEarlyApplicant to point students at listings with less competition, and applyBy to prioritise deadlines. The perks array highlights which listings include certificates and letters of recommendation that matter for academic credit.

📊 Labour market research on early careers

postedAt, openings, and duration together form the basis of a hiring-volume time series. Tracking total openings per category month over month gives researchers, policy analysts, and journalists a measurable indicator of graduate hiring demand that is otherwise very difficult to source in India.

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⚙️ Tips for Better Internshala Scraping Results

• Run internships and jobs as separate jobs. The two listingType values have different compensation semantics — monthly stipends versus salaries — and mixing them in one dataset makes any aggregate figure misleading.
• Disable scrapeDetails for large exploratory sweeps. Listing-level data is enough to map the shape of the market. Turn detail scraping back on once you have narrowed to the categories and cities that matter.
• Use minStipend rather than filtering afterwards. Filtering at search level cuts crawl volume, whereas post-filtering means you paid to fetch listings you then discarded.
• Keep maxPages in mind on multi-filter runs. The page cap applies per filter combination, so a run with five categories and four cities crawls far more pages than a single unfiltered run at the same setting.
• Raise requestDelay before lowering maxConcurrency. A slightly longer gap between requests is usually a gentler and more effective stabiliser on large crawls than cutting parallelism outright.
• Deduplicate on listingId across scheduled runs. Listings persist for weeks, so consecutive runs overlap heavily. listingId plus scrapedAt gives you both a clean unique set and a full observation history.

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🛠️ Troubleshooting

skills, perks, and description are empty in my dataset. Those three fields come from the listing detail pages. Confirm scrapeDetails is set to true — with it disabled the Internshala scraper only reads search result pages, which do not contain the full description, skill list, or perk list.

I got far fewer listings than maxListings allows. Your filters are probably narrower than you intended. Combining a specific category, a specific city, workFromHome, partTime, and a high minStipend can reduce the matching pool to a handful of listings. Relax one filter at a time to see which is the binding constraint. maxPages may also be capping the crawl before maxListings is reached.

Stipend fields are zero for listings that look paid. stipendMin and stipendMax are parsed from the displayed stipend text. Listings using non-numeric wording such as performance-based or negotiable compensation cannot produce numeric bounds, so they return zeros while the original text remains in stipend. Filter on isUnpaid and on a non-zero stipendMax when building compensation statistics.

A large run slowed down or produced failures partway through. Reduce crawl pressure: increase requestDelay towards 1000–1500 ms and lower maxConcurrency to two. Also consider splitting one very large run into several smaller runs partitioned by city or category.

Results include listings outside the cities I specified. Remote listings are commonly surfaced across city searches because work-from-home roles are not tied to a location. Cross-check isRemote and location when you need strictly location-based results, and set workFromHome to false if you want to exclude remote roles from a city study.

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❓ Frequently Asked Questions About Internshala Scraping

What does the Internshala Internship & Jobs Scraper extract? It extracts internship and job listings from Internshala with 26 fields per listing, covering title, company, location, stipend, duration, deadlines, openings, skills, perks, description, and platform hiring signals.

Can it scrape both internships and jobs? Yes, through the listingType field, which accepts internships or jobs. Run it twice if you need both, since the two sections report compensation differently.

How do I scrape only remote internships? Set workFromHome to true. Each returned record also carries an isRemote boolean you can verify against.

Is stipend data numeric or text? Both. stipend holds the original displayed text, while stipendMin and stipendMax hold parsed numeric INR values, and isUnpaid flags zero-stipend listings.

How many listings can the Internshala scraper collect in one run? maxListings controls the cap and accepts 0 for unlimited. In practice the total is also bounded by maxPages and by how many listings match your filters.

What is the difference between maxListings and maxPages? maxListings caps the total number of records saved across the run. maxPages caps how many search result pages are crawled per filter combination. Whichever limit is reached first stops that part of the crawl.

Do I need to configure a proxy? No. Proxy rotation is handled automatically inside the Actor and there is no proxy field in the input schema.

Does the scraper use a headless browser? No. It fetches and parses pages directly over HTTP, which keeps runs fast and memory-light even on large crawls.

What is isEarlyApplicant useful for? It flags listings where applying now still counts as an early application, which generally means fewer competing candidates. Career services teams use it to prioritise which listings to promote to students.

What does hasJobOffer mean? It indicates that the internship advertises a job offer — a pre-placement offer — as one of its perks, which is a strong signal of employer intent to convert interns into full-time hires.

How do I avoid duplicate listings across scheduled runs? Deduplicate on listingId. Keep scrapedAt alongside it if you want an observation history rather than a single current-state row per listing.

Can I export Internshala data to Google Sheets? Yes. Apify datasets export to JSON, CSV, Excel, XML, and HTML, and the Actor integrates with Google Sheets, Zapier, Make, and Slack, plus webhooks on run completion.

How current is the data? Each record reflects the listing as it appeared when the run executed, recorded in scrapedAt. Use postedAt to judge how recently the employer published the role, and schedule regular runs to keep a dataset current.

Can I filter by minimum stipend? Yes, using minStipend, expressed in INR per month. Filtering at search level is more efficient than collecting everything and discarding low-paying listings afterwards.

Can I request a custom version of the Internshala jobs scraper? Yes. Email scraperhubapi@gmail.com to discuss additional fields, different filter logic, or integration with your own ATS or data warehouse.

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🆘 Support & Feedback

If the Internshala scraper misses listings, returns unexpected values, or a run fails, report it on the Issues tab of the Actor page. Include your input configuration and the run ID so the behaviour can be reproduced.

For custom scraping work — larger recruitment data pipelines, additional fields, or a tailored version of this Internshala jobs scraper for your own hiring analytics — email scraperhubapi@gmail.com.

If this Actor helps your recruitment research, please leave a review on the Apify Store. Ratings and written feedback shape which improvements are prioritised.

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⚖️ Disclaimer

The Internshala Internship & Jobs Scraper collects publicly visible internship and job listings. It does not log in, bypass authentication, or access candidate applications, private employer dashboards, or any content behind a login.

You are responsible for how you use the recruitment data this Internshala scraper produces. Comply with Internshala's terms of service, crawl at reasonable rates using requestDelay and maxConcurrency, and respect the platform's technical limits. Job listings occasionally contain personal information such as an individual recruiter's name; where that is the case, data protection law — including the GDPR where it applies, and India's own data protection framework — governs your handling of it, and you must establish a lawful basis, honour data subject rights, and apply appropriate retention limits.

Listing data is provided as-is and reflects the state of the platform at the moment recorded in scrapedAt. Verify stipends, deadlines, and openings against the live listing before making hiring, application, or publication decisions.

If you believe data returned by this Actor should be removed or corrected, email scraperhubapi@gmail.com with the details and the request will be reviewed.