Wellfound Data API: Extract Structured JSON in 2026

This guide covers extracting publicly accessible data. Always review a site's robots.txt and Terms of Service before scraping.

TL;DR

Use AlterLab's Extract API to pull structured JSON from Wellfound job pages. Define a JSON schema for the fields you need, POST the URL and schema, and receive typed, validated data — no HTML parsing required. The approach works for single pages or large‑scale pagination jobs.

Why use Wellfound data?

Wellfound hosts a rich set of startup and tech job listings that are valuable for several engineering workflows:

• AI training: Collect real‑world job descriptions to fine‑tune language models for career‑related tasks.
• Market analytics: Track salary trends, demand for specific skills, or geographic distribution of roles.
• Competitive intelligence: Monitor which companies are hiring for particular stacks or roles to inform product or go‑to‑market strategies.

All of these rely on publicly visible job cards; no login or paywall is needed.

What data can you extract?

From a typical Wellfound job page you can pull the following fields, all of which appear in the public HTML:

• job_title – the role title (e.g., "Senior Software Engineer")
• company – the hiring organization name
• location – city, state, or remote indicator
• salary – listed compensation range or exact figure
• posted_date – when the listing went live
• employment_type – full‑time, contract, internship, etc.

Each field returns as a string; you can enforce types or formats via the JSON schema you supply to the Extract API.

The extraction approach

Attempting to scrape Wellfound with raw HTTP requests and HTML parsers runs into several obstacles:

• Frequent layout changes break CSS selectors.
• JavaScript‑rendered content requires a headless browser.
• Anti‑bot mechanisms (rate limits, CAPTCHAs) trigger blocks.
• Maintaining parsers across dozens of pages becomes fragile.

A data API like AlterLab abstracts these concerns. It handles proxy rotation, JavaScript rendering, and anti‑bot bypass, then returns the data you asked for in the exact shape you defined. You interact with a clean HTTP endpoint rather than wrestling with HTML.

Quick start with AlterLab Extract API

First, install the AlterLab Python client (or use cURL directly). The Extract API endpoint is /v1/extract. Below is a minimal Python example that pulls a single job posting.

import alterlab

client = alterlab.Client("YOUR_API_KEY")

schema = {
  "type": "object",
  "properties": {
    "job_title": {
      "type": "string",
      "description": "The job title field"
    },
    "company": {
      "type": "string",
      "description": "The company field"
    },
    "location": {
      "type": "string",
      "description": "The location field"
    },
    "salary": {
      "type": "string",
      "description": "The salary field"
    },
    "posted_date": {
      "type": "string",
      "description": "The posted date field"
    },
    "employment_type": {
      "type": "string",
      "description": "The employment type field"
    }
  }
}

result = client.extract(
    url="https://wellfound.com/jobs/example-software-engineer",
    schema=schema,
)
print(result.data)

The highlighted lines (5‑12) show the schema definition. The call returns a JSON object whose keys match the schema properties.

Here is the equivalent cURL request:

curl -X POST https://api.alterlab.io/v1/extract \
  -H "X-API-Key: YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://wellfound.com/jobs/example-software-engineer",
    "schema": {"properties": {"job_title": {"type": "string"}, "company": {"type": "string"}, "location": {"type": "string"}}}
  }'

Both examples produce a response like:

{
  "job_title": "Senior Software Engineer",
  "company": "Acme Corp",
  "location": "San Francisco, CA",
  "salary": "$150,000 - $180,000",
  "posted_date": "2024-09-10",
  "employment_type": "Full-time"
}

Infographic: Stats Grid

Define your schema

The power of the Extract API lies in schema‑driven validation. You supply a JSON Schema that describes the expected shape; AlterLab attempts to extract matching content and returns a typed object. If a field cannot be found, it appears as null (or you can add "default" values). This eliminates guesswork and downstream cleaning.

schema = {
  "type": "object",
  "properties": {
    "job_title": {"type": "string"},
    "company": {"type": "string"},
    "location": {"type": "string"},
    "salary": {"type": "string"},
    "posted_date": {"type": "string", "format": "date"},
    "employment_type": {"type": "string", "enum": ["Full-time", "Part-time", "Contract", "Internship"]}
  },
  "required": ["job_title", "company", "location"]
}

When you pass this schema to client.extract, the API validates the output against it. You receive a guarantee that every returned job object conforms to the structure you expect, making it safe to feed directly into databases or machine learning pipelines.

Infographic: Step Flow