# Mercor Jobs Search Scraper (`alexist/mercor-jobs-search-scraper`) Actor Scrape job listings from Mercor's talent marketplace with precision. Collect 50+ fields including role details, company info, matching scores, interview configs, and compensation data — perfect for talent intelligence, market research, and platform integration. - **URL**: https://apify.com/alexist/mercor-jobs-search-scraper.md - **Developed by:** [Alex](https://apify.com/alexist) (community) - **Categories:** Automation, Developer tools, Jobs - **Stats:** 2 total users, 1 monthly users, 100.0% 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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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 ## Mercor Jobs Scraper: Extract Talent Marketplace Listings at Scale --- ### What Is Mercor? Mercor is a premier talent marketplace connecting vetted engineers and technical professionals with high-growth tech companies. Unlike traditional job boards, Mercor uses intelligent matching algorithms to connect candidates with opportunities based on skills, preferences, and career goals. For recruiters, researchers, and developers, accessing Mercor's job data at scale unlocks competitive intelligence and market trends — the **Mercor Jobs Search Scraper** automates this extraction process. --- ### Overview The **Mercor Jobs Search Scraper** extracts job listings from Mercor's explore page and search results, delivering rich, structured records with 50+ data points per listing. It's built for: - **Recruiters** monitoring tech hiring trends and competitor activity - **Talent strategists** benchmarking compensation, equity, and role types - **Market researchers** studying remote-first and distributed work adoption - **Job aggregators** feeding Mercor data into multi-platform job boards - **Data analysts** building models on tech labor supply and demand Key features include configurable item limits, flexible URL input for targeted scraping, and robust error handling via `ignore_url_failures`. --- ### Input Format The scraper accepts a JSON configuration object: ```json { "urls": [ "https://work.mercor.com/explore" ], "ignore_url_failures": true, "max_items_per_url": 20 } ```` | Field | Description | Example | |---|---|---| | `urls` | Array of Mercor jobs list or explore page URLs | `["https://work.mercor.com/explore"]` | | `max_items_per_url` | Maximum listings to extract per URL (default: 20) | `20`, `50`, `100` | | `ignore_url_failures` | If `true`, continues scraping if a URL fails; if `false`, halts the run | `true` or `false` | **URL Tips:** - Use `https://work.mercor.com/explore` to scrape all active listings - Add URL filters for specific parameters (e.g., location, commitment type) if Mercor's UI supports them - Each URL is processed independently; run multiple URLs for broader data collection *** ### Output Format **Sample output** ```json { "listing_id": "list_AAABn2QixPUZ3VRzf41C26mh", "version": 5, "uid": "listing_uid_AAABn2QkZPr3Vi5LrEJDrqE5", "title": "Clinical Law Professor / Clinic Director", "description": "Mercor is seeking experienced **Clinical Law Professors and Clinic Directors** to evaluate AI-generated legal reasoning involving civil legal services. Experts will combine doctrinal knowledge, legal pedagogy, and practical supervision experience to assess AI-generated legal analyses.\n\n* * *\n\n## Responsibilities\n\n- Evaluate AI-generated legal responses.\n \n- Review issue spotting, legal reasoning, and procedural analysis.\n \n- Assess scenarios involving housing, family, consumer, and debt law.\n \n- Provide structured feedback to improve AI systems.\n \n- Apply experience supervising law students and live-client clinics.\n \n\n* * *\n\n## Required Qualifications\n\n- Juris Doctor (JD).\n \n- Current or previous U.S. bar admission.\n \n- Experience directing or supervising a law school clinic.\n \n- Expertise in one or more civil legal practice areas.\n \n- Strong legal writing and evaluation skills.\n \n\n* * *\n\n## Preferred Qualifications\n\n- Clinical Professor of Law or Clinic Director.\n \n- Experience with housing, family, consumer, elder, or disability law clinics.\n \n- Experience developing legal curriculum or assessment rubrics.\n \n- Published scholarship or educational materials.\n \n\n* * *\n\n## Why Join Mercor?\n\n- Help build more accurate AI for legal education and practice.\n \n- Bring your clinical teaching expertise to emerging legal technologies.\n \n- Short-term, intellectually engaging project.\n \n- Competitive compensation.\n \n\n* * *\n\n## Engagement Details\n\n**Location:** Remote\n\n**Commitment:** Approximately 15 hours/week\n\n**Project Duration:** Estimated 2–3 weeks", "commitment": "hourly", "referral_amount": 720.0, "created_at": "2026-07-15T04:57:08", "deleted_at": null, "is_most_recent": true, "status": "active", "listing_type": "standard", "required_interview_config_id": null, "rate_min": 180.0, "rate_max": 180.0, "hours_per_week": 40, "location": "Remote", "work_arrangement": "remote", "eligible_location": [ "USA" ], "eligible_residence_location": [ "USA" ], "ineligible_location": [], "ineligible_residence_location": [], "form_id": null, "disable_applications": false, "auto_redirect_to_apply": false, "pay_rate_frequency": "hourly", "is_private": false, "interview_duration": null, "offers_equity": false, "interview_scheduling_enabled": false, "interview_schedule_link": null, "is_recommended": false, "hourly_pay_rate": null, "company_brand_visible": false, "company_logo": null, "company_name": null, "company_alias": null, "posted_at": "2026-07-15T04:57:08", "user_similarity_score": 0.0, "search_similarity_score": 0.0, "match_quality": null, "prequalified_listing": false, "recent_candidates_count": 0, "application_steps_completed": null, "application_steps_pending": null, "listing_domain": "Law", "remaining_slots": 2, "supplied_slots": 0, "max_future_hcg": 2, "is_fast_paced_enabled": false, "fast_paced_role_id": null, "submitted_on": null, "from_url": "https://work.mercor.com/explore" } ``` Each listing returns a comprehensive record with 50+ fields organized by category: #### Listing Identification | Field | Meaning | |---|---| | `Listing ID` | Unique identifier for the job posting on Mercor | | `UID` | Universal identifier linked across Mercor systems | | `Version` | Data schema version of the listing record | | `Listing Type` | Category of job (e.g., full-time, contract, freelance) | | `Posted At` | Timestamp when the listing was published | | `Submitted On` | When the listing data was submitted/updated | | `Is Most Recent` | Boolean indicating if this is the current version | | `Status` | Current state of the listing (active, closed, draft) | | `Deleted At` | Timestamp if the listing was deleted | #### Job Details & Description | Field | Meaning | |---|---| | `Title` | Job title as displayed to candidates | | `Description` | Full job description, qualifications, and responsibilities | | `Commitment` | Work commitment type (full-time, part-time, contract, freelance) | | `Hours Per Week` | Expected weekly hours for the role | | `Work Arrangement` | Employment arrangement (remote, hybrid, on-site) | | `Pay Rate Frequency` | How compensation is billed (hourly, monthly, yearly) | | `Hourly Pay Rate` | Standardized hourly equivalent rate in USD | | `Rate Min` | Minimum compensation/hourly rate | | `Rate Max` | Maximum compensation/hourly rate | | `Offers Equity` | Boolean indicating if the role includes equity | #### Location & Eligibility | Field | Meaning | |---|---| | `Location` | Primary job location or timezone | | `Eligible Location` | Geographic regions where candidates are eligible | | `Eligible Residence Location` | Countries/regions where candidates may reside | | `Ineligible Location` | Restricted geographic locations | | `Ineligible Residence Location` | Prohibited residence regions (often for tax/visa compliance) | | `Listing Domain` | Domain or department category the listing belongs to | #### Matching & Scoring | Field | Meaning | |---|---| | `User Similarity Score` | Mercor's match score between candidate and job (0-100) | | `Search Similarity Score` | Relevance score for search query matching | | `Match Quality` | Overall quality of the match (low, medium, high, excellent) | | `Is Recommended` | Mercor's internal recommendation flag for this listing | | `Prequalified Listing` | Whether candidates are pre-screened before application | #### Application & Interview Flow | Field | Meaning | |---|---| | `Form ID` | Unique identifier for the application form | | `Application Steps Completed` | Number of application steps already filled by candidate | | `Application Steps Pending` | Remaining application steps required | | `Disable Applications` | Boolean to pause new applications | | `Auto Redirect To Apply` | Whether clicking the listing auto-initiates application flow | | `Required Interview Config ID` | ID of the interview process template for this role | | `Interview Duration` | Expected interview length in minutes | | `Interview Scheduling Enabled` | Whether candidates can self-schedule interviews | | `Interview Schedule Link` | URL for candidates to schedule an interview | #### Company Information | Field | Meaning | |---|---| | `Company Name` | Official company name posting the job | | `Company Alias` | Alternative name or brand used by the company | | `Company Logo` | URL to the company's logo image | | `Company Brand Visible` | Boolean indicating if company branding is shown to candidates | #### Performance & Capacity | Field | Meaning | |---|---| | `Recent Candidates Count` | Number of applicants in the last period (e.g., 7 days) | | `Remaining Slots` | Number of open positions still available | | `Supplied Slots` | Total positions the company provided | | `Max Future HCG` | Maximum future hiring cohort groups planned | | `Is Fast Paced Enabled` | Boolean for "fast-paced role" designation | | `Fast Paced Role ID` | Identifier if the role is in fast-paced hiring pipeline | #### Referral & Incentives | Field | Meaning | |---|---| | `Referral Amount` | Bonus paid for successful candidate referrals | #### Privacy & Visibility | Field | Meaning | |---|---| | `Is Private` | Whether the listing is private (not visible to all users) | *** ### How to Use 1. **Identify target URLs** — Navigate to Mercor's explore page or use filtered search results. Copy the URL. 2. **Set scrape parameters** — Adjust `max_items_per_url` based on your needs (20–100 typically; higher values increase runtime). 3. **Configure resilience** — Set `ignore_url_failures: true` for production runs to skip unreachable URLs. 4. **Execute the scraper** — Start the actor and monitor progress via the run log. 5. **Download results** — Export data as JSON, CSV, or Excel for analysis. **Best Practices:** - Scrape during off-peak hours to minimize impact on Mercor's platform - Use filters in the URL to target specific regions, commitment types, or skill categories - Set `max_items_per_url` to 20–30 for consistent, repeatable runs - Combine multiple scrapes to build time-series datasets for trend analysis **Common Issues:** - If few items are returned, verify the URL contains active listings and is accessible - Check Mercor's robots.txt and terms of service before large-scale scraping - Use `ignore_url_failures: true` to handle transient network issues gracefully *** ### Use Cases & Business Value - **Talent intelligence:** Track hiring velocity, compensation benchmarks, and equity offerings across tech companies - **Competitive analysis:** Monitor when competitors post roles, hiring volume, and target markets - **Platform integration:** Feed Mercor data into internal job boards or talent aggregators - **Market research:** Analyze remote-work trends, skill demand, and geographic hiring patterns - **Compensation benchmarking:** Build datasets for salary and rate comparison across roles and regions By automating data collection, the Mercor Jobs Scraper enables data-driven talent strategy while eliminating manual research hours. *** ### Conclusion The **Mercor Jobs Search Scraper** delivers comprehensive, structured data from a leading tech talent marketplace. With 50+ fields covering job details, matching insights, company info, and application workflows, it's the ideal solution for recruiters, researchers, and talent platform operators. Start scraping today and gain competitive edge through data-driven hiring intelligence. # Actor input Schema ## `urls` (type: `array`): Add the URLs of the Jobs list urls you want to scrape. You can paste URLs one by one, or use the Bulk edit section to add a prepared list. ## `ignore_url_failures` (type: `boolean`): If true, the scraper will continue running even if some URLs fail to be scraped. ## `max_items_per_url` (type: `integer`): The maximum number of items to scrape per URL. ## Actor input object example ```json { "urls": [ "https://work.mercor.com/explore" ], "ignore_url_failures": true, "max_items_per_url": 20 } ``` # 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 = { "urls": [ "https://work.mercor.com/explore" ], "ignore_url_failures": true, "max_items_per_url": 20 }; // Run the Actor and wait for it to finish const run = await client.actor("alexist/mercor-jobs-search-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 = { "urls": ["https://work.mercor.com/explore"], "ignore_url_failures": True, "max_items_per_url": 20, } # Run the Actor and wait for it to finish run = client.actor("alexist/mercor-jobs-search-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 '{ "urls": [ "https://work.mercor.com/explore" ], "ignore_url_failures": true, "max_items_per_url": 20 }' | apify call alexist/mercor-jobs-search-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=alexist/mercor-jobs-search-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Mercor Jobs Search Scraper", "description": "Scrape job listings from Mercor's talent marketplace with precision. Collect 50+ fields including role details, company info, matching scores, interview configs, and compensation data — perfect for talent intelligence, market research, and platform integration.", "version": "0.0", "x-build-id": "H8yIgXZrs6Eyd7DGI" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/alexist~mercor-jobs-search-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-alexist-mercor-jobs-search-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/alexist~mercor-jobs-search-scraper/runs": { "post": { "operationId": "runs-sync-alexist-mercor-jobs-search-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/alexist~mercor-jobs-search-scraper/run-sync": { "post": { "operationId": "run-sync-alexist-mercor-jobs-search-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": { "urls": { "title": "URLs of the Jobs list urls to scrape", "type": "array", "description": "Add the URLs of the Jobs list urls you want to scrape. You can paste URLs one by one, or use the Bulk edit section to add a prepared list.", "items": { "type": "string" } }, "ignore_url_failures": { "title": "Continue running even if some URLs fail to be scraped", "type": "boolean", "description": "If true, the scraper will continue running even if some URLs fail to be scraped." }, "max_items_per_url": { "title": "Max items per URL", "type": "integer", "description": "The maximum number of items to scrape per URL." } } }, "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 } } } } } } } } } } ```