# Lever Jobs Scraper (`enezli/lever-scraper`) Actor Scrape any company's Lever job board and get clean, de-duplicated job JSON: title, team, location, workplace type and canonical apply URLs. One click, no required fields, no LLM. - **URL**: https://apify.com/enezli/lever-scraper.md - **Developed by:** [Turgay NANTA](https://apify.com/enezli) (community) - **Categories:** Agents, MCP servers, Jobs - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 Scrape any company's Lever job board and get clean, de-duplicated job JSON: title, team, location, workplace type and canonical apply URLs. One click, no required fields, no LLM. ### What it does Lever (jobs.lever.co) hosts the careers pages of thousands of tech companies. This actor reads any company's **public Lever postings API** and returns every open role as clean JSON: title, team, location, commitment, workplace type and the canonical hosted-apply URL. Give it one company handle (`palantir`) or several (`palantir,figma`) and it paginates through everything, de-duplicated and ready for your pipeline. The perfect twin to our Greenhouse Jobs Scraper for full ATS coverage. **Why this one:** - **Official public postings API** — no HTML parsing, no proxies - **Team, commitment and workplace type included** - **Multi-company runs + monitor mode for scheduled diffing** - **No LLM anywhere** — deterministic output, predictable costs, no hallucinated fields - **Clean by default** — canonical URLs (tracking parameters stripped), parsed numbers, merged duplicates ### Quick start (no code) 1. Click **Try for free** / **Start** — every field has a working default, nothing is required. 2. (Optional) change **query** to what you need. 3. Open the **Dataset** tab when the run finishes → export as JSON, CSV or Excel. ### Input | Field | Required | Default | Description | |---|---|---|---| | `query` | no | `palantir` | Lever company handle, comma-separated for multiple (e.g. `palantir,figma`). Find it in the job page URL: `jobs.lever.co/`. | | `maxResults` | no | `20` | Maximum clean results (capped at 500) | | `enrich` | no | `false` | Deterministic enrichment per record — see below | | `monitor` | no | `false` | Compare with the previous run, flag NEW records only | Example input: ```json { "query": "palantir,figma", "maxResults": 100 } ```` ### Output Real example record (from a live run): ```json { "id": "ac978161-6f46-4f6b-ad9e-a258e642751c", "title": "Administrative Business Partner", "url": "https://jobs.lever.co/palantir/ac978161-6f46-4f6b-ad9e-a258e642751c", "location": "London, United Kingdom", "seller": "palantir", "team": "Administrative", "commitment": "Full-time", "description": "A World-Changing Company Palantir builds the world's leading software for data-driven decisions..." } ``` The final `_summary` row carries run totals (`total_clean`, `deduped`, `enriched`); in monitor mode a `_changes` row lists keys new since the last run. **Field reference** | Field | Meaning | |---|---| | `id` | Lever posting ID (stable, deduplication key) | | `title` | Job title | | `url` | Canonical hosted apply URL | | `seller` | Company handle | | `location` | Posting location(s) | | `team` | Team / department | | `commitment` | Full-time / part-time etc. | | `workplace_type / country / created_at` | Present when the company publishes them | | `price` / `price_text` | Parsed numeric value + original text, when the source publishes one | | `completeness` | 0–1 filled-fields score (with `enrich`) | ### Use cases - **ATS coverage** — pair with our Greenhouse Jobs Scraper to cover the two biggest startup ATS platforms. - **Competitor hiring watch** — schedule with monitor mode; `change-alert` fires only on NEW roles. - **Talent mapping** — which teams is a scale-up growing? `team` tells you directly. - **Sales signals** — new go-to-market roles = expansion signal for your CRM. - **AI agents** — live 'is company X hiring in Europe?' answers via MCP. ### Enrichment (optional, charged only when it produces something) Set `enrich: true` and every record additionally gets: e-mail addresses extracted from the description (when present), the canonical `domain` of the record's URL, and a `completeness` score (0–1, how many core fields are filled). Deterministic — the same input always yields the same output — and you are **only charged for records that actually got enriched**. Records where enrichment adds nothing are free. ### Monitor mode — change alerts on a schedule Set `monitor: true` and the actor compares the current run with the previous one (per-actor named storage) and flags **only NEW records**. Combine with [Apify Schedules](https://docs.apify.com/platform/schedules) for a daily/hourly watch: the `_changes` summary row lists what appeared since the last run, and the `change-alert` event is charged **per new record only** — an unchanged run costs you almost nothing. ### Use it from your code **Python** ```python from apify_client import ApifyClient client = ApifyClient("") run = client.actor("EnezLi/lever-scraper").call(run_input={ "query": "palantir,figma", "maxResults": 100 }) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) ``` **JavaScript** ```javascript import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: '' }); const { defaultDatasetId } = await client.actor('EnezLi/lever-scraper').call({ "query": "palantir,figma", "maxResults": 100 }); const { items } = await client.dataset(defaultDatasetId).listItems(); console.log(items); ``` **curl** ```bash curl -X POST "https://api.apify.com/v2/acts/EnezLi~lever-scraper/run-sync-get-dataset-items?token=" \ -H "Content-Type: application/json" \ -d '{ "query": "palantir,figma", "maxResults": 100 }' ``` ### Use it with AI agents (MCP) This actor is agent-ready: it appears in Apify's **AGENTS / MCP servers** catalog, so any MCP-capable assistant (Claude, custom agents, LangGraph tools) can discover and call it with a one-line tool call — zero required fields means an agent can run it safely with defaults. Connect your agent to the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp) and ask for live data in natural language. ### Pricing — Pay-Per-Event, start is free | Event | When charged | |---|---| | Actor start | **Free ($0)** — try it with one click | | `result` | Per clean result returned | | `enrichment` | Only per record that actually got enriched | | `change-alert` | Monitor mode: per NEW record since the previous run | No subscription, no minimum. Volume discounts apply automatically through Apify account tiers (up to −44% on GOLD). Typical run cost example: 20 results ≈ a few cents total — you can predict your bill from the numbers above before you run. ### Is this legal? This actor collects **publicly available data only** — the same information any visitor sees in a browser, via public endpoints. It does not bypass logins, collect private personal data, or store credentials. You are responsible for using the output in compliance with the source site's terms and the laws that apply to you (e.g. GDPR when the output contains personal data). ### Support & feedback Found a bug, need another field, or want a variant for a related platform? **Open an issue on the Issues tab** — issues are monitored and answered, and frequently-requested fields get added to the standard output. The actor is maintained as part of a scraper family built on one shared, tested core (bugs fixed once are fixed everywhere). ### Changelog - **0.1** (2026-07) — initial public release: search, dedup, optional enrichment, monitor mode, PPE pricing. ### Limitations (honest ones) Reads Lever's official public postings API; companies that left Lever or hid their board return zero rows. Description text is truncated to 500 characters for now. ### FAQ **How do I find a company's Lever handle?** Open the company's careers page; if it's on Lever the URL looks like `jobs.lever.co/palantir` — the last part is the handle. **A company returned zero jobs — why?** Either the handle is misspelled, the company has no open roles, or they moved off Lever. The run ends cleanly either way (no crash, nothing charged for results). **Full job descriptions?** A 500-character plain-text excerpt is included; full descriptions are a planned add-on — open an issue if you need them. **Do I need an API key or account on the source platform?** No. The actor uses public endpoints — you only need your Apify account. **Does it use AI / an LLM?** No. The core is fully deterministic: same input, same output, no hallucinations, no per-token costs. **Can I run it on a schedule?** Yes — use Apify Schedules; combine with monitor mode to pay only for what's new. **What's the maximum number of results?** 500 per run (memory-safe cap). Run multiple queries or schedule runs for more. **How is my bill calculated?** Only from the events in the Pricing table — start is free, and there is no subscription. # Actor input Schema ## `query` (type: `string`): Lever company handle, comma-separated for multiple (e.g. 'palantir,figma'). Find it in the job page URL: jobs.lever.co/. Leave empty to run the default example — no required fields. ## `maxResults` (type: `integer`): Maximum number of clean results to return (capped at 500). ## `enrich` (type: `boolean`): Adds deterministic enrichment per record: extracted emails, canonical domain and a completeness score. Charged per enriched record (see Pricing). ## `monitor` (type: `boolean`): Compares this run with the previous one and flags NEW records only. Ideal for scheduled runs — get alerted when something changes. Charged per change. ## Actor input object example ```json { "query": "palantir", "maxResults": 20, "enrich": false, "monitor": false } ``` # 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 = { "query": "palantir" }; // Run the Actor and wait for it to finish const run = await client.actor("enezli/lever-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 = { "query": "palantir" } # Run the Actor and wait for it to finish run = client.actor("enezli/lever-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 '{ "query": "palantir" }' | apify call enezli/lever-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=enezli/lever-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Lever Jobs Scraper", "description": "Scrape any company's Lever job board and get clean, de-duplicated job JSON: title, team, location, workplace type and canonical apply URLs. One click, no required fields, no LLM.", "version": "0.1", "x-build-id": "Na7DVCeDSfP9Oamky" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/enezli~lever-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-enezli-lever-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/enezli~lever-scraper/runs": { "post": { "operationId": "runs-sync-enezli-lever-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/enezli~lever-scraper/run-sync": { "post": { "operationId": "run-sync-enezli-lever-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "query": { "title": "Company name(s) on Lever", "type": "string", "description": "Lever company handle, comma-separated for multiple (e.g. 'palantir,figma'). Find it in the job page URL: jobs.lever.co/. Leave empty to run the default example — no required fields." }, "maxResults": { "title": "Max results", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum number of clean results to return (capped at 500).", "default": 20 }, "enrich": { "title": "Enrich results", "type": "boolean", "description": "Adds deterministic enrichment per record: extracted emails, canonical domain and a completeness score. Charged per enriched record (see Pricing).", "default": false }, "monitor": { "title": "Monitor mode (change alerts)", "type": "boolean", "description": "Compares this run with the previous one and flags NEW records only. Ideal for scheduled runs — get alerted when something changes. Charged per change.", "default": false } } }, "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 } } } } } } } } } } ```