# Levels.fyi Salary Scraper (`ninja.dev/levels-fyi-salary-scraper`) Actor Scrape tech salary and total-compensation data from Levels.fyi by company and role. Returns per-level pay breakdowns (base, stock, bonus, total, median, percentiles) plus individual offer samples, with built-in new-offer detection to track pay changes over time. - **URL**: https://apify.com/ninja.dev/levels-fyi-salary-scraper.md - **Developed by:** [Matheus Coelho](https://apify.com/ninja.dev) (community) - **Categories:** Developer tools, Lead generation, Jobs - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.28 / 1,000 salary sample scrapeds This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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/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 ## Levels.fyi Salary Scraper — Tech Compensation Data by Company & Role Need to know **what a company actually pays**? This actor pulls tech **salary and total-compensation data from Levels.fyi** for any company and role — the base, stock, bonus, and total comp **broken down by level**, plus individual offer samples with years of experience, location, and offer date. Give it a list of companies (`google`, `amazon`, `meta`, ...) and a role (`software-engineer`, `product-manager`, ...) and get back clean, structured, exportable compensation data — ready for benchmarking, recruiting, or negotiation research. ### Why this scraper - **Per-level pay breakdown, ready-made** — every run returns a compensation summary for each level (base, stock, bonus, total comp, sample count, and a link to the level page). Most scrapers just dump raw rows; this one hands you the intelligence layer buyers actually want. - **Compensation monitoring built in** — with **Detect new offers** on (it is by default), every run remembers the offers it has already seen per company/role and flags brand-new ones with `isNew: true`, plus a count of new offers. Run it on a schedule and you'll see the day pay shifts at a target company. No other Levels.fyi scraper tracks this across runs. - **Fast & cheap** — reads the data straight from the page instead of driving a browser, so runs finish in seconds. - **Rich, granular data** — title, level, focus area, years of experience, years at company, location, base / stock / bonus / total comp, and offer date for every sample. - **Country filter** — restrict results to a single country when you need local benchmarks. ### How to use it (30 seconds) 1. Enter one or more **companies** (Levels.fyi URL slugs, e.g. `google`). 2. Set the **role** (default `software-engineer`) and press **Start**. 3. Download the results as JSON, CSV, or Excel — or pipe them into Make, Zapier, n8n, Google Sheets, or your own webhook via Apify integrations. To track pay over time, save your input as a task and add a **schedule** (e.g. weekly). Each run reports how many offers are new since the last one. ### Input | Field | Type | Description | |---|---|---| | `companies` | array | Company slugs from the Levels.fyi URL (e.g. `google`, `amazon`). | | `jobFamily` | string | Role slug (default `software-engineer`). | | `country` | string | Optional Levels.fyi numeric country id (e.g. `254` = US, `43` = Canada). | | `maxSamplesPerCompany` | integer | Max individual samples per company (default 200). | | `includeSamples` | boolean | Return individual offers, not just aggregates (default true). | | `detectNewOffers` | boolean | Flag new offers vs. last run (default true). | ### Output Two record types. A **per-level summary** (returned in full regardless of the sample cap): ```json { "recordType": "levelSummary", "company": "google", "role": "software-engineer", "level": "l3", "levelName": "SWE II", "sampleCount": 33, "base": 161788, "stock": 34280, "bonus": 8702, "totalCompensation": 204770, "levelPageUrl": "https://www.levels.fyi/companies/google/salaries/software-engineer/levels/l3" } ```` And individual **offer samples**: ```json { "recordType": "sample", "company": "google", "role": "software-engineer", "title": "Software Engineer", "level": "L3", "yearsOfExperience": 1, "location": "Los Angeles, CA", "offerDate": "2026-07-06T16:12:08.865+00:00", "baseSalary": 140000, "stock": 20000, "bonus": 0, "totalCompensation": 160000, "isNew": true } ``` Each run also saves a compact summary (levels, samples, and new offers per company) as the run's `OUTPUT` — handy when an integration only needs to know *"did pay change?"* without reading the whole dataset. ### What people use it for #### Benchmark compensation across companies Feed in a list of competitors and one role to compare total comp level-by-level — exactly what you need to set or defend a pay band. #### Get alerted when pay shifts at a target company Schedule a run with **Detect new offers** on. When `totalNewOffers > 0`, trigger a Slack/email alert through an Apify integration and see fresh offers as they land. #### Recruiting & talent-market intelligence Recruiters and staffing teams: know what candidates are really being offered at each level before making an offer, and spot where the market is moving. #### Negotiation research Going into an offer conversation? Pull the level breakdown and recent samples for the company and role so you walk in with real numbers. #### Comp & HR analytics at scale Build your own compensation dataset across dozens of companies and roles for dashboards, reports, or models — exported straight to CSV or your warehouse. ### FAQ #### Which companies and roles can I scrape? Any company and role that exists on Levels.fyi. Use the exact slugs from the URL: `levels.fyi/companies//salaries/` — e.g. company `amazon`, role `product-manager`. #### What's the difference between the level summary and the samples? The **level summary** is the aggregate pay for each level (average base, stock, bonus, total, and how many data points it's based on). The **samples** are the individual anonymized offers that make up those levels, with per-offer detail. #### How is this better than browsing Levels.fyi myself? Manually you look at one company at a time. The actor pulls dozens of companies in one run, exports everything to JSON/CSV, runs on a schedule, and — the part the website can't do — remembers previous runs so it can tell you **which offers are new**. #### Do I need a Levels.fyi account, API key, or proxies? No. Everything runs inside the actor — no login, no API key, no proxy setup. #### Can I filter by country? Yes. Set the `country` input to a Levels.fyi numeric country id to restrict results to that country (leave it empty for worldwide). #### Can it send alerts automatically? Yes. Combine an Apify **schedule** with an **integration** (Slack, email, Make, Zapier, n8n, or webhook) that fires when the run summary shows new offers. ### Support & feedback Found a bug or want another role/field? Open a ticket in the **Issues** tab — I usually respond within a few hours and actor updates ship fast. If this actor saves you time, please leave a ⭐ **review** on the Store page. It takes 10 seconds, genuinely helps others find it, and tells me what to build next. ### Legal & ethical use This actor extracts only publicly available, anonymized compensation data that Levels.fyi already publishes — no logins, no private data, no personal identities. You are responsible for using the data in compliance with applicable laws and the terms of service that apply to you. # Actor input Schema ## `companies` (type: `array`): Company slugs as they appear in a Levels.fyi URL (levels.fyi/companies/). E.g. "google", "amazon", "meta", "microsoft". ## `jobFamily` (type: `string`): Role slug as it appears in a Levels.fyi URL (levels.fyi/companies//salaries/). E.g. "software-engineer", "product-manager", "data-scientist", "engineering-manager". ## `country` (type: `string`): Optional Levels.fyi numeric country id to restrict results to one country (e.g. "254" = United States, "43" = Canada). Leave empty for worldwide (default). ## `maxSamplesPerCompany` (type: `integer`): Maximum individual salary samples to scrape per company/role. Per-level aggregates are always returned in full regardless of this cap. ## `includeSamples` (type: `boolean`): Return individual offer records (title, level, years of experience, location, base/stock/bonus/total, offer date). Turn off to return only the per-level aggregate breakdown. ## `detectNewOffers` (type: `boolean`): Remember offers seen in previous runs and flag brand-new ones with isNew=true, plus a summary of how many are new since last run. Ideal for tracking when pay shifts at a target company. Uses a persistent named store. ## Actor input object example ```json { "companies": [ "google", "amazon" ], "jobFamily": "software-engineer", "maxSamplesPerCompany": 200, "includeSamples": true, "detectNewOffers": 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": [ "google", "amazon" ] }; // Run the Actor and wait for it to finish const run = await client.actor("ninja.dev/levels-fyi-salary-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": [ "google", "amazon", ] } # Run the Actor and wait for it to finish run = client.actor("ninja.dev/levels-fyi-salary-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": [ "google", "amazon" ] }' | apify call ninja.dev/levels-fyi-salary-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=ninja.dev/levels-fyi-salary-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Levels.fyi Salary Scraper", "description": "Scrape tech salary and total-compensation data from Levels.fyi by company and role. Returns per-level pay breakdowns (base, stock, bonus, total, median, percentiles) plus individual offer samples, with built-in new-offer detection to track pay changes over time.", "version": "0.1", "x-build-id": "mfDlmmXT44zEZGZMk" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/ninja.dev~levels-fyi-salary-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-ninja.dev-levels-fyi-salary-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/ninja.dev~levels-fyi-salary-scraper/runs": { "post": { "operationId": "runs-sync-ninja.dev-levels-fyi-salary-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/ninja.dev~levels-fyi-salary-scraper/run-sync": { "post": { "operationId": "run-sync-ninja.dev-levels-fyi-salary-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": "Companies", "type": "array", "description": "Company slugs as they appear in a Levels.fyi URL (levels.fyi/companies/). E.g. \"google\", \"amazon\", \"meta\", \"microsoft\".", "items": { "type": "string" } }, "jobFamily": { "title": "Role (job family)", "type": "string", "description": "Role slug as it appears in a Levels.fyi URL (levels.fyi/companies//salaries/). E.g. \"software-engineer\", \"product-manager\", \"data-scientist\", \"engineering-manager\".", "default": "software-engineer" }, "country": { "title": "Country filter (optional)", "type": "string", "description": "Optional Levels.fyi numeric country id to restrict results to one country (e.g. \"254\" = United States, \"43\" = Canada). Leave empty for worldwide (default)." }, "maxSamplesPerCompany": { "title": "Max salary samples per company", "minimum": 1, "type": "integer", "description": "Maximum individual salary samples to scrape per company/role. Per-level aggregates are always returned in full regardless of this cap.", "default": 200 }, "includeSamples": { "title": "Include individual salary samples", "type": "boolean", "description": "Return individual offer records (title, level, years of experience, location, base/stock/bonus/total, offer date). Turn off to return only the per-level aggregate breakdown.", "default": true }, "detectNewOffers": { "title": "Detect new offers (compensation monitoring)", "type": "boolean", "description": "Remember offers seen in previous runs and flag brand-new ones with isNew=true, plus a summary of how many are new since last run. Ideal for tracking when pay shifts at a target company. Uses a persistent named store.", "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 } } } } } } } } } } ```