What Does BrowserAct Lever Job Details Scraper Do?
Use BrowserAct Lever Job Details Scraper to collect complete public job details from one Lever posting, including job_title, company_name, description, salary, location. The Bot stays within the selected public Lever page flow and returns structured job details for job aggregation, role monitoring, or downstream automation without writing code or requiring a target-site login. Run it in BrowserAct or invoke the same published template through its API Tab.
Key Features of Lever Job Details Scraper
- Configure
job_urlto reuse the same Bot for different public Lever targets. - Collect complete public job details from one Lever posting from the template's defined public page flow.
- Return structured job details with fields such as
job_title,company_name,location,team,employment_type. - Run without a target-site account login or custom scraper code.
- Use the API Tab to connect the same published template to downstream automation.
What Data Can I Extract from Lever?
| Field | Description |
|---|---|
job_id | Job Id returned from the public Lever page. |
job_title | Visible job title shown on Lever. |
company_name | Visible company name shown on Lever. |
location | Publicly displayed location when available. |
team | Team returned from the public Lever page. |
employment_type | Employment Type returned from the public Lever page. |
workplace_type | Workplace Type returned from the public Lever page. |
description | Visible description returned by the public page. |
responsibilities | Responsibilities returned from the public Lever page. |
requirements | Requirements returned from the public Lever page. |
additional_qualifications | Additional Qualifications returned from the public Lever page. |
salary | Structured salary extracted from the public source. |
benefits | Benefits returned from the public Lever page. |
deadline | Deadline returned from the public Lever page. |
company_logo_url | Public source URL associated with the job details. |
job_url | Public source URL associated with the job details. |
How to Use Lever Job Details Scraper
Quick Start Guide
1. Open Run Task.
2. Review or change the populated input values.
3. Run the Bot.
4. Review the structured result in the Output Tab.
5. Connect the published template to your downstream workflow when needed.
Input Parameters
| Parameter | Configuration |
|---|---|
job_url | String; required; default https://jobs.lever.co/spotify/47d4eabf-8d5a-4dab-ac72-de7b5b56841c. Public job url to process. |
Example Output
The table shows one representative real result. Long text is shortened only in this documentation preview.
| Field | Result |
|---|---|
job_id | 47d4eabf-8d5a-4dab-ac72-de7b5b56841c |
job_title | Senior Staff Machine Learning Engineer - Content Platform |
company_name | Spotify |
location | New York, NY |
team | Engineering – Experience / |
employment_type | Permanent / |
workplace_type | Remote |
description | We design Spotify’s consumer experience—end to end, moment to moment, across every screen, platform, and partner integration. Our mission is to make listening feel effortless, personal, and joyful for billions of user... |
responsibilities | Define and drive machine learning strategy across content understanding, safety, policy enforcement, and platform-level decisioning Build and scale production ML systems for classification, moderation, ranking, risk d... |
requirements | You have significant experience designing and building production-grade machine learning systems at scale You have deep experience with modern ML frameworks such as PyTorch, TensorFlow, JAX, or similar You have experi... |
additional_qualifications | null |
salary | The United States base range for this position is $281,196 - $401,709 plus equity. These ranges may be modified in the future. |
benefits | The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays, paid sick leave. |
deadline | null |
company_logo_url | https://lever-client-logos.s3.us-west-2.amazonaws.com/0f50cbb4-b98b-444e-bf90-e2e61463d146-1738001993645.png |
job_url | https://jobs.lever.co/spotify/47d4eabf-8d5a-4dab-ac72-de7b5b56841c |
How Does It Work?
1. BrowserAct validates the supplied inputs and maps supported dropdown selections.
2. The Bot constructs or normalizes the target URL and opens the supplied public detail page.
3. It reads the visible job details without entering unrelated pages or account-only areas.
4. It normalizes visible values, preserves valid optional values, and stops after the target page has been processed.
5. The run returns one structured response for review, export, or downstream automation.
How to Build a New Scraper Bot with Agent Built
Step 1: Describe the data you need
Open BrowserAct and start Agent Built from the Home prompt box or from Create > Build with Agent. Describe the public website, records, filters or search conditions, output fields, result limit, and reusable inputs you need.
Step 2: Let BrowserAct build and test the Bot
BrowserAct explores the live website, creates the navigation and extraction workflow, and tests the resulting Bot. Provide any requested clarification about the target page, conditions, fields, or expected output in the same build conversation.
Step 3: Run the Bot and review the result
Run the completed Bot with its populated default inputs. Review the structured records, source URLs, missing optional values, and result count before reusing the Bot with other inputs or connecting it to another workflow.
Use Cases
- Job aggregation.
- Role monitoring.
- Recruiting research.
- Connect the structured result to API clients, n8n, Make, Zapier, MCP-based agents, or supported exports.
Why Use Lever Job Details Scraper?
- Reuse a tested public-page workflow instead of configuring the extraction from the beginning.
- Receive structured job details that can move directly into research, review, or automation.
- Change supported inputs without rebuilding the Bot for every run.
Who Can Use Lever Job Details Scraper?
- Teams working on job aggregation.
- Teams working on role monitoring.
- Teams working on recruiting research.
- Developers and automation teams connecting public web data to downstream systems.
How Many Results Can You Scrape?
Each run processes one selected public target and returns one structured result for that target.
Automation and Export
BrowserAct produces structured data first. Use the result through Run Task, the JSON API, n8n, Make, Zapier, MCP-based agents, or the supported manual export flow.
Need Help?
Cannot find the exact capability you need, or need help composing several Bots?
