One API for Greenhouse, Lever, Ashby & Workday Jobs: Hiring Signals in Python

Job postings are among the most honest public data a company produces. A company hiring three data engineers is Most of these jobs live on applicant tracking systems (ATS) such as Greenhouse, Lever, Ashby, Workable, This guide pulls jobs straight from the source with one call and one schema. Career Site Jobs Scraper on Apify: input: company domains (stripe.com), ATS board URLs (boards.greenhouse.io/stripe, jobs.lever.co/palantir, jobs.ashbyhq.com/ramp), or Workday URLs. It auto-detects the ATS from the company website; output: one normalized row per job, with title, department, team, location(s), isRemote, workplaceType, employmentType, seniority, salary range when published, postedAt, apply URL and an optional clean-text description; filters: title keywords (include/exclude), locations, remote only, posted within N days; price: $6 per 1,000 jobs, with filtered-out jobs free. import collections, os from apify_client import ApifyClient companies = ["stripe.com", "huggingface.co", "notion.so", "ramp.com", "https://jobs.lever.co/palantir"] client = ApifyClient(os.environ["APIFY_TOKEN"]) run = client.actor("murch1k/career-site-jobs-scraper").call(run_input={ "companies": companies, "keywords": ["data", "machine learning", "analytics"], "excludeKeywords": ["intern"], "postedWithinDays": 30, "includeDescription": False, "maxResults": 500, # hard cost cap }) jobs = list(client.dataset(run["defaultDatasetId"]).iterate_items()) by_company = collections.Counter(j["companyName"] for j in jobs) for company, n in by_company.most_common(): print(f"{company:25} {n} open data roles") A row (shortened, values illustrative): { "companyName": "Hugging Face", "ats": "workable", "title": "Machine Learning Engineer - Open Source", "location": "Remote, Europe", "isRemote": true, "workplaceType": "remote", "employmentType": "full-time", "salaryMin": null, "postedAt": "2026-10-02T00:00:00.000Z", "url": "https://apply.workable.com/huggingface/j/…" } 1. Sales trigger list. Run weekly over your target-account list with keywords that match your product 2. Niche job board / newsletter. Pick 200 companies in a niche (climate tech, Rust, AI safety), schedule a daily run postedWithinDays: 1, and publish the feed. Every posting links back to the original apply URL. 3. Market research for LLMs. Turn on includeDescription and let a model extract the tech stack, seniority mix and { "mcpServers": { "apify": { "url": "https://mcp.apify.com/?tools=murch1k/career-site-jobs-scraper", "headers": { "Authorization": "Bearer <APIFY_TOKEN>" } } } } Ask Claude or Cursor: "Which of these 20 companies are hiring ML engineers in Europe right now? Group by company." Unknown ATS: if a company uses an in-house careers page, auto-detection returns nothing and you are not charged. Salary: only present when the employer publishes it (common in US states with pay-transparency laws). Dates: some ATSs don't expose a posting date. Those jobs are kept even with a postedWithinDays filter. Full Python and Node.js examples are in the Apify Scrapers Pack repo, alongside scrapers for LinkedIn All five are pay-per-result Actors on Apify (no subscription; the Apify free plan's monthly credits cover a test run): YouTube Transcript Scraper: transcripts with timestamps from videos, channels and playlists. Career Site Jobs Scraper: open roles from Greenhouse, Lever, Ashby, Workday and 4 more ATSs. Google Trends Scraper: interest over time, related queries and topics. Substack Scraper: posts and newsletter metadata as clean text for LLMs. Website Contact Details Extractor: emails, phones and socials, one row per domain. Python, Node.js, LangChain and MCP examples for each one are in apify-scrapers-pack on GitHub. A star there helps other people find it.