JOB MARKET & HR

Salary Benchmarking Data

Collect compensation data from job postings, salary sites, and company pages. Make data-driven compensation decisions.

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Compensation Data is Fragmented

Accurate salary data is essential for competitive compensation packages, but information is scattered across job boards, company sites, and salary databases. Manual collection doesn't scale.

Traditional salary surveys are expensive and often outdated by the time they're published. You need real-time market data.

WebScraping.AI Solution

  • Job Posting Data: Extract salary ranges from live job listings
  • AI Extraction: Intelligently parse compensation details from any format
  • Multi-Source: Aggregate data from multiple job boards and salary sites
  • Real-Time Updates: Always current market data

Salary Data Points

Comprehensive compensation intelligence

Base Salary

Extract salary ranges, minimums, maximums, and midpoints from listings.

Total Compensation

Capture bonus, equity, benefits, and other compensation components.

Location Data

Track salary variations by city, state, region, and remote status.

Level & Experience

Segment by seniority level, years of experience, and skills required.

Code Examples

Extract salary data from job postings

Extract salary data from a job posting
curl -G "https://api.webscraping.ai/ai/fields" \
  --data-urlencode "api_key=YOUR_API_KEY" \
  --data-urlencode "url=https://job-board.com/job/senior-software-engineer" \
  --data-urlencode "fields[job_title]=The job title" \
  --data-urlencode "fields[company]=Company name" \
  --data-urlencode "fields[location]=Job location (city, state, remote)" \
  --data-urlencode "fields[salary_min]=Minimum salary as a number" \
  --data-urlencode "fields[salary_max]=Maximum salary as a number" \
  --data-urlencode "fields[salary_currency]=Currency (USD, EUR, etc)" \
  --data-urlencode "fields[salary_period]=Annual, monthly, or hourly" \
  --data-urlencode "fields[bonus]=Bonus information if mentioned" \
  --data-urlencode "fields[equity]=Equity/stock options if mentioned" \
  --data-urlencode "fields[benefits]=Benefits listed" \
  --data-urlencode "fields[experience_required]=Years of experience required" \
  --data-urlencode "fields[seniority_level]=Junior, Mid, Senior, Lead, etc"
# Response:
# {
#   "job_title": "Senior Software Engineer",
#   "company": "Tech Corp",
#   "location": "San Francisco, CA (Hybrid)",
#   "salary_min": 180000,
#   "salary_max": 220000,
#   "salary_currency": "USD",
#   "salary_period": "annual",
#   "bonus": "15-20% annual bonus",
#   "equity": "Stock options available",
#   "benefits": ["Health insurance", "401k match", "Unlimited PTO"],
#   "experience_required": "5+ years",
#   "seniority_level": "Senior"
# }
# pip install webscraping_ai
# https://pypi.org/project/webscraping-ai/
from webscraping_ai import Client

client = Client(api_key="YOUR_API_KEY")
result = client.fields(
    "https://job-board.com/job/senior-software-engineer",
    fields={
        "job_title": "The job title",
        "company": "Company name",
        "location": "Job location (city, state, remote)",
        "salary_min": "Minimum salary as a number",
        "salary_max": "Maximum salary as a number",
        "salary_currency": "Currency (USD, EUR, etc)",
        "salary_period": "Annual, monthly, or hourly",
        "bonus": "Bonus information if mentioned",
        "equity": "Equity/stock options if mentioned",
        "benefits": "Benefits listed",
        "experience_required": "Years of experience required",
        "seniority_level": "Junior, Mid, Senior, Lead, etc",
    },
)
print(result)
# Response:
# {
#   "job_title": "Senior Software Engineer",
#   "company": "Tech Corp",
#   "location": "San Francisco, CA (Hybrid)",
#   "salary_min": 180000,
#   "salary_max": 220000,
#   "salary_currency": "USD",
#   "salary_period": "annual",
#   "bonus": "15-20% annual bonus",
#   "equity": "Stock options available",
#   "benefits": ["Health insurance", "401k match", "Unlimited PTO"],
#   "experience_required": "5+ years",
#   "seniority_level": "Senior"
# }
// npm install webscraping-ai
// https://www.npmjs.com/package/webscraping-ai
import { WebScrapingAI } from 'webscraping-ai';

const client = new WebScrapingAI({ apiKey: 'YOUR_API_KEY' });
const result = await client.fields({
  url: 'https://job-board.com/job/senior-software-engineer',
  fields: {
    job_title: 'The job title',
    company: 'Company name',
    location: 'Job location (city, state, remote)',
    salary_min: 'Minimum salary as a number',
    salary_max: 'Maximum salary as a number',
    salary_currency: 'Currency (USD, EUR, etc)',
    salary_period: 'Annual, monthly, or hourly',
    bonus: 'Bonus information if mentioned',
    equity: 'Equity/stock options if mentioned',
    benefits: 'Benefits listed',
    experience_required: 'Years of experience required',
    seniority_level: 'Junior, Mid, Senior, Lead, etc',
  },
});
console.log(result);
// Response:
// {
//   "job_title": "Senior Software Engineer",
//   "company": "Tech Corp",
//   "location": "San Francisco, CA (Hybrid)",
//   "salary_min": 180000,
//   "salary_max": 220000,
//   "salary_currency": "USD",
//   "salary_period": "annual",
//   "bonus": "15-20% annual bonus",
//   "equity": "Stock options available",
//   "benefits": ["Health insurance", "401k match", "Unlimited PTO"],
//   "experience_required": "5+ years",
//   "seniority_level": "Senior"
// }
<?php
// composer require webscraping-ai/webscraping-ai-php
// https://packagist.org/packages/webscraping-ai/webscraping-ai-php
require 'vendor/autoload.php';

use WebScrapingAI\Client;

$client = new Client('YOUR_API_KEY');
$result = $client->fields('https://job-board.com/job/senior-software-engineer', [
    'job_title'           => 'The job title',
    'company'             => 'Company name',
    'location'            => 'Job location (city, state, remote)',
    'salary_min'          => 'Minimum salary as a number',
    'salary_max'          => 'Maximum salary as a number',
    'salary_currency'     => 'Currency (USD, EUR, etc)',
    'salary_period'       => 'Annual, monthly, or hourly',
    'bonus'               => 'Bonus information if mentioned',
    'equity'              => 'Equity/stock options if mentioned',
    'benefits'            => 'Benefits listed',
    'experience_required' => 'Years of experience required',
    'seniority_level'     => 'Junior, Mid, Senior, Lead, etc',
]);
print_r($result);
// Response:
// {
//   "job_title": "Senior Software Engineer",
//   "company": "Tech Corp",
//   "location": "San Francisco, CA (Hybrid)",
//   "salary_min": 180000,
//   "salary_max": 220000,
//   "salary_currency": "USD",
//   "salary_period": "annual",
//   "bonus": "15-20% annual bonus",
//   "equity": "Stock options available",
//   "benefits": ["Health insurance", "401k match", "Unlimited PTO"],
//   "experience_required": "5+ years",
//   "seniority_level": "Senior"
// }
# gem install webscraping_ai
# https://rubygems.org/gems/webscraping_ai
require 'webscraping_ai'

client = WebScrapingAI::Client.new(api_key: 'YOUR_API_KEY')
result = client.fields(
  'https://job-board.com/job/senior-software-engineer',
  fields: {
    job_title:            'The job title',
    company:              'Company name',
    location:             'Job location (city, state, remote)',
    salary_min:           'Minimum salary as a number',
    salary_max:           'Maximum salary as a number',
    salary_currency:      'Currency (USD, EUR, etc)',
    salary_period:        'Annual, monthly, or hourly',
    bonus:                'Bonus information if mentioned',
    equity:               'Equity/stock options if mentioned',
    benefits:             'Benefits listed',
    experience_required:  'Years of experience required',
    seniority_level:      'Junior, Mid, Senior, Lead, etc',
  }
)
puts result.inspect
# Response:
# {
#   "job_title": "Senior Software Engineer",
#   "company": "Tech Corp",
#   "location": "San Francisco, CA (Hybrid)",
#   "salary_min": 180000,
#   "salary_max": 220000,
#   "salary_currency": "USD",
#   "salary_period": "annual",
#   "bonus": "15-20% annual bonus",
#   "equity": "Stock options available",
#   "benefits": ["Health insurance", "401k match", "Unlimited PTO"],
#   "experience_required": "5+ years",
#   "seniority_level": "Senior"
# }
// go get github.com/webscraping-ai/webscraping-ai-go/v4
// https://pkg.go.dev/github.com/webscraping-ai/webscraping-ai-go/v4
package main

import (
    "context"
    "fmt"

    webscrapingai "github.com/webscraping-ai/webscraping-ai-go/v4"
)

func main() {
    client, _ := webscrapingai.NewClient(&webscrapingai.Config{APIKey: "YOUR_API_KEY"})
    result, _ := client.Fields(context.Background(), &webscrapingai.FieldsOptions{
        URL: "https://job-board.com/job/senior-software-engineer",
        Fields: map[string]string{
            "job_title": "The job title",
            "company": "Company name",
            "location": "Job location (city, state, remote)",
            "salary_min": "Minimum salary as a number",
            "salary_max": "Maximum salary as a number",
            "salary_currency": "Currency (USD, EUR, etc)",
            "salary_period": "Annual, monthly, or hourly",
            "bonus": "Bonus information if mentioned",
            "equity": "Equity/stock options if mentioned",
            "benefits": "Benefits listed",
            "experience_required": "Years of experience required",
            "seniority_level": "Junior, Mid, Senior, Lead, etc",
        },
    })
    fmt.Println(result.Result)
}
// Response:
// {
//   "job_title": "Senior Software Engineer",
//   "company": "Tech Corp",
//   "location": "San Francisco, CA (Hybrid)",
//   "salary_min": 180000,
//   "salary_max": 220000,
//   "salary_currency": "USD",
//   "salary_period": "annual",
//   "bonus": "15-20% annual bonus",
//   "equity": "Stock options available",
//   "benefits": ["Health insurance", "401k match", "Unlimited PTO"],
//   "experience_required": "5+ years",
//   "seniority_level": "Senior"
// }
// Maven: ai.webscraping:webscraping-ai:4.0.0
// https://central.sonatype.com/artifact/ai.webscraping/webscraping-ai
import ai.webscraping.Client;
import ai.webscraping.Config;
import ai.webscraping.option.FieldsOptions;
import ai.webscraping.result.FieldsResult;

Client client = new Client(Config.builder().apiKey("YOUR_API_KEY").build());
FieldsResult result = client.fields(FieldsOptions.builder()
    .url("https://job-board.com/job/senior-software-engineer")
    .addField("job_title", "The job title")
    .addField("company", "Company name")
    .addField("location", "Job location (city, state, remote)")
    .addField("salary_min", "Minimum salary as a number")
    .addField("salary_max", "Maximum salary as a number")
    .addField("salary_currency", "Currency (USD, EUR, etc)")
    .addField("salary_period", "Annual, monthly, or hourly")
    .addField("bonus", "Bonus information if mentioned")
    .addField("equity", "Equity/stock options if mentioned")
    .addField("benefits", "Benefits listed")
    .addField("experience_required", "Years of experience required")
    .addField("seniority_level", "Junior, Mid, Senior, Lead, etc")
    .build());
System.out.println(result.getResult());
// Response:
// {
//   "job_title": "Senior Software Engineer",
//   "company": "Tech Corp",
//   "location": "San Francisco, CA (Hybrid)",
//   "salary_min": 180000,
//   "salary_max": 220000,
//   "salary_currency": "USD",
//   "salary_period": "annual",
//   "bonus": "15-20% annual bonus",
//   "equity": "Stock options available",
//   "benefits": ["Health insurance", "401k match", "Unlimited PTO"],
//   "experience_required": "5+ years",
//   "seniority_level": "Senior"
// }
// dotnet add package WebScrapingAI
// https://www.nuget.org/packages/WebScrapingAI
using WebScrapingAI;

var client = new WebScrapingAIClient(new WebScrapingAIClientOptions { ApiKey = "YOUR_API_KEY" });
var result = await client.FieldsAsync(new FieldsRequest {
    Url = "https://job-board.com/job/senior-software-engineer",
    Fields = new Dictionary<string, string> {
        ["job_title"] = "The job title",
        ["company"] = "Company name",
        ["location"] = "Job location (city, state, remote)",
        ["salary_min"] = "Minimum salary as a number",
        ["salary_max"] = "Maximum salary as a number",
        ["salary_currency"] = "Currency (USD, EUR, etc)",
        ["salary_period"] = "Annual, monthly, or hourly",
        ["bonus"] = "Bonus information if mentioned",
        ["equity"] = "Equity/stock options if mentioned",
        ["benefits"] = "Benefits listed",
        ["experience_required"] = "Years of experience required",
        ["seniority_level"] = "Junior, Mid, Senior, Lead, etc",
    },
});
Console.WriteLine(result.Result);
// Response:
// {
//   "job_title": "Senior Software Engineer",
//   "company": "Tech Corp",
//   "location": "San Francisco, CA (Hybrid)",
//   "salary_min": 180000,
//   "salary_max": 220000,
//   "salary_currency": "USD",
//   "salary_period": "annual",
//   "bonus": "15-20% annual bonus",
//   "equity": "Stock options available",
//   "benefits": ["Health insurance", "401k match", "Unlimited PTO"],
//   "experience_required": "5+ years",
//   "seniority_level": "Senior"
// }
Ask the AI to benchmark competitiveness
curl -G "https://api.webscraping.ai/ai/question" \
  --data-urlencode "api_key=YOUR_API_KEY" \
  --data-urlencode "url=https://job-board.com/job/senior-software-engineer" \
  --data-urlencode "question=How does this compensation package compare to market rates? What are the standout benefits?"

# pip install webscraping_ai
# https://pypi.org/project/webscraping-ai/
from webscraping_ai import Client

client = Client(api_key="YOUR_API_KEY")
answer = client.question(
    "https://job-board.com/job/senior-software-engineer",
    question="How does this compensation package compare to market rates? What are the standout benefits?",
)
print(answer)

// npm install webscraping-ai
// https://www.npmjs.com/package/webscraping-ai
import { WebScrapingAI } from 'webscraping-ai';

const client = new WebScrapingAI({ apiKey: 'YOUR_API_KEY' });
const answer = await client.question({
  url: 'https://job-board.com/job/senior-software-engineer',
  question: 'How does this compensation package compare to market rates? What are the standout benefits?',
});
console.log(answer);

<?php
// composer require webscraping-ai/webscraping-ai-php
// https://packagist.org/packages/webscraping-ai/webscraping-ai-php
require 'vendor/autoload.php';

use WebScrapingAI\Client;

$client = new Client('YOUR_API_KEY');
$answer = $client->question(
    'https://job-board.com/job/senior-software-engineer',
    'How does this compensation package compare to market rates? What are the standout benefits?',
);
echo $answer;

# gem install webscraping_ai
# https://rubygems.org/gems/webscraping_ai
require 'webscraping_ai'

client = WebScrapingAI::Client.new(api_key: 'YOUR_API_KEY')
answer = client.question(
  'https://job-board.com/job/senior-software-engineer',
  question: 'How does this compensation package compare to market rates? What are the standout benefits?'
)
puts answer

// go get github.com/webscraping-ai/webscraping-ai-go/v4
// https://pkg.go.dev/github.com/webscraping-ai/webscraping-ai-go/v4
package main

import (
    "context"
    "fmt"

    webscrapingai "github.com/webscraping-ai/webscraping-ai-go/v4"
)

func main() {
    client, _ := webscrapingai.NewClient(&webscrapingai.Config{APIKey: "YOUR_API_KEY"})
    answer, _ := client.Question(context.Background(), &webscrapingai.QuestionOptions{
        URL:      "https://job-board.com/job/senior-software-engineer",
        Question: "How does this compensation package compare to market rates? What are the standout benefits?",
    })
    fmt.Println(answer)
}

// Maven: ai.webscraping:webscraping-ai:4.0.0
// https://central.sonatype.com/artifact/ai.webscraping/webscraping-ai
import ai.webscraping.Client;
import ai.webscraping.Config;
import ai.webscraping.option.QuestionOptions;

Client client = new Client(Config.builder().apiKey("YOUR_API_KEY").build());
String answer = client.question(QuestionOptions.builder()
    .url("https://job-board.com/job/senior-software-engineer")
    .question("How does this compensation package compare to market rates? What are the standout benefits?")
    .build());
System.out.println(answer);

// dotnet add package WebScrapingAI
// https://www.nuget.org/packages/WebScrapingAI
using WebScrapingAI;

var client = new WebScrapingAIClient(new WebScrapingAIClientOptions { ApiKey = "YOUR_API_KEY" });
var answer = await client.QuestionAsync(new QuestionRequest {
    Url = "https://job-board.com/job/senior-software-engineer",
    Question = "How does this compensation package compare to market rates? What are the standout benefits?",
});
Console.WriteLine(answer);

Why Use WebScraping.AI

Real-Time Data: Current market rates, not outdated survey data.
Comprehensive Coverage: Data from multiple sources for accuracy.
Custom Segmentation: Filter by role, location, level, industry.
AI Parsing: Handles any salary format automatically.
Structured Output: Clean data ready for analysis.

Use Cases

Compensation Planning

Set competitive salary bands and ranges

Talent Acquisition

Make competitive offers to top candidates

Retention Analysis

Identify roles at risk of market-driven attrition

Market Research

Understand compensation trends by industry

Related Use Cases

More HR data solutions

Job Listing Aggregation

Aggregate job postings from multiple boards.

Company Reviews

Monitor employee reviews and sentiment.

Lead Generation

Find companies and contacts.

Start Collecting Salary Data

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