# Lever Jobs Scraper (`nexgendata/lever-jobs-scraper`) Actor Scrape Lever-hosted job postings by company slug — title, team, department, commitment, location, country, workplace type, plain-text and HTML descriptions, apply URL, posted date. Recruiter sourcing, ATS competitive analytics, sales prospecting on companies actively hiring. - **URL**: https://apify.com/nexgendata/lever-jobs-scraper.md - **Developed by:** [Stephan Corbeil](https://apify.com/nexgendata) (community) - **Categories:** Jobs, Business - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $50.00 / 1,000 job postings This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## 🎯 Lever Jobs Scraper — Open API Postings Scrape Lever-hosted job boards in bulk via Lever's official open postings API. Feed in a list of company slugs (the path segment in `jobs.lever.co/{slug}`), get back every live posting with title, team, department, location, plain-text and HTML descriptions, and apply URL. Built for **recruiters and sourcing tools** (track who's hiring senior roles), **ATS competitive analysts** (Lever vs. Greenhouse vs. Ashby adoption tracking), **sales/BD teams** (prospect into companies actively scaling teams), and **job aggregators / talent platforms** that need clean structured postings. ### What you get per posting - `id`, `company_slug` — Lever's posting ID + the slug you fed in - `title` — job title - `team`, `department`, `commitment` — categorical breakdown (e.g. Engineering, Backend, Full-Time) - `location`, `all_locations`, `country`, `workplace_type` — onsite / remote / hybrid + geographic data - `hosted_url` — canonical Lever-hosted job page - `apply_url` — direct apply link - `created_at`, `updated_at` — Unix-ms timestamps - `tags` — any tags Lever exposes - `description_html`, `description_plain` — main body in both formats - `opening_html`, `opening_plain` — intro paragraph (often pitch / mission) - `description_body_html`, `description_body_plain` — main responsibilities + qualifications - `lists` — Lever's structured lists (Responsibilities, Qualifications, etc.) - `additional_html`, `additional_plain` — equal-opportunity, comp band, perks - `source` — `"lever.co"` ### Use cases 1. **Recruiter sourcing** — pull every senior IC role at 75 Lever-hosted Series B/C companies and search descriptions for tech-stack keywords. 2. **ATS market share research** — count active Lever postings per company per quarter to track Lever vs. Greenhouse adoption. 3. **Sales prospecting** — companies that just added a "Head of Engineering" or "VP Sales" role are prime ICP for many B2B tools. 4. **Job aggregator backfill** — feed your remote-jobs board with structured Lever data refreshed nightly. 5. **Comp-band benchmarking** — many Lever postings publish salary ranges in the description; extract them downstream. 6. **Investor / portfolio monitoring** — track headcount growth at portfolio cos. via posting cadence. ### Quick start Input: ```json { "companies": ["clari", "figma", "box"], "maxJobs": 200, "includeContent": true } ```` Sample output row: ```json { "id": "abc123-...-def", "company_slug": "clari", "title": "Senior Backend Engineer, Platform", "team": "Engineering", "department": "Platform", "commitment": "Full-Time", "location": "Sunnyvale, CA", "country": "United States", "workplace_type": "hybrid", "hosted_url": "https://jobs.lever.co/clari/abc123", "apply_url": "https://jobs.lever.co/clari/abc123/apply", "created_at": 1738473600000, "updated_at": 1745030400000, "description_plain": "Clari is hiring...", "source": "lever.co" } ``` ### Run via Python (apify-client) ```python from apify_client import ApifyClient client = ApifyClient("YOUR_APIFY_TOKEN") run = client.actor("nexgendata/lever-jobs-scraper").call( run_input={ "companies": ["clari", "figma", "box"], "maxJobs": 500, "includeContent": True, } ) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item["title"], "-", item["company_slug"], "-", item["location"]) ``` ### Run via cURL ```bash curl -X POST "https://api.apify.com/v2/acts/nexgendata~lever-jobs-scraper/run-sync-get-dataset-items?token=YOUR_TOKEN" \ -H "Content-Type: application/json" \ -d '{"companies":["clari","figma"],"maxJobs":50}' ``` ### Integrations - **Zapier** — fire on each new posting, push into Airtable / Notion / Slack DM. - **Make.com** — pipe new postings into HubSpot or Salesforce as new prospect signals. - **n8n** — schedule + dedupe + push into Postgres for trend analysis. ### Pricing Pay-per-event (PPE): - **Actor start:** $0.00005 (negligible) - **Per posting:** **$0.05** Cost calculator: | Postings returned | Cost | | --- | --- | | 10 (smoke test) | $0.50 | | 100 | $5.00 | | 1,000 | $50.00 | | 5,000 (multi-company sweep) | $250.00 | You only pay for postings actually pushed to your dataset — failed companies don't get charged. ### FAQ **Q: Where do I find the Lever slug?** A: Visit the company's careers page. If the URL is `jobs.lever.co/{slug}` (or it redirects to one), `{slug}` is what you want. **Q: What about Greenhouse / Ashby / Workday?** A: Different ATS, different actors. See Related Actors below. **Q: Do I need a Lever API key?** A: No. The public postings API at `api.lever.co/v0/postings/{slug}?mode=json` requires no auth. **Q: How fresh is the data?** A: Real-time. Postings show up within minutes of the company adding them in Lever. **Q: What if a company moved off Lever?** A: You'll get an empty array (logged) and zero charges for that company. The actor moves on. **Q: Can I get just the metadata?** A: Yes — set `includeContent: false`. Cuts row size by 80–95%. ### Related actors - [Greenhouse Jobs Scraper](https://apify.com/nexgendata/greenhouse-jobs-scraper) — same shape, for Greenhouse-hosted boards. - [WeWorkRemotely Jobs Scraper](https://apify.com/nexgendata/weworkremotely-jobs-scraper) — remote-first job aggregator. - [HN Who's Hiring Scraper](https://apify.com/nexgendata/hn-whos-hiring-scraper) — monthly Hacker News hiring threads. *** Built and maintained by [NexGenData](https://apify.com/nexgendata?fpr=2ayu9b) — affordable, focused web scrapers for B2B data teams. # Actor input Schema ## `companies` (type: `array`): List of Lever-hosted company slugs. The slug is the path segment in `https://jobs.lever.co/{slug}` — e.g. `clari`, `figma`, `box`, `palantir`. Each company yields all of its currently live postings. ## `maxJobs` (type: `integer`): Maximum total number of postings to return across all companies (1–5000). ## `includeContent` (type: `boolean`): If checked, includes the HTML and plain-text body of each posting (description, opening, lists, additional). Uncheck for slim metadata-only output. ## Actor input object example ```json { "companies": [ "clari", "figma", "box" ], "maxJobs": 100, "includeContent": true } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "companies": [ "clari", "figma", "box" ], "maxJobs": 100, "includeContent": true }; // Run the Actor and wait for it to finish const run = await client.actor("nexgendata/lever-jobs-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "companies": [ "clari", "figma", "box", ], "maxJobs": 100, "includeContent": True, } # Run the Actor and wait for it to finish run = client.actor("nexgendata/lever-jobs-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "companies": [ "clari", "figma", "box" ], "maxJobs": 100, "includeContent": true }' | apify call nexgendata/lever-jobs-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nexgendata/lever-jobs-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Lever Jobs Scraper", "description": "Scrape Lever-hosted job postings by company slug — title, team, department, commitment, location, country, workplace type, plain-text and HTML descriptions, apply URL, posted date. Recruiter sourcing, ATS competitive analytics, sales prospecting on companies actively hiring.", "version": "0.0", "x-build-id": "A08oi027PS9FkMnGd" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nexgendata~lever-jobs-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nexgendata-lever-jobs-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/nexgendata~lever-jobs-scraper/runs": { "post": { "operationId": "runs-sync-nexgendata-lever-jobs-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/nexgendata~lever-jobs-scraper/run-sync": { "post": { "operationId": "run-sync-nexgendata-lever-jobs-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "companies" ], "properties": { "companies": { "title": "Lever company slugs", "type": "array", "description": "List of Lever-hosted company slugs. The slug is the path segment in `https://jobs.lever.co/{slug}` — e.g. `clari`, `figma`, `box`, `palantir`. Each company yields all of its currently live postings.", "items": { "type": "string" } }, "maxJobs": { "title": "Max postings total", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Maximum total number of postings to return across all companies (1–5000).", "default": 200 }, "includeContent": { "title": "Include full description content", "type": "boolean", "description": "If checked, includes the HTML and plain-text body of each posting (description, opening, lists, additional). Uncheck for slim metadata-only output.", "default": true } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```