I built an interactive map of jobs at top AI companies

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I built a live interactive map that shows where top AI companies hire around the world. I collected this data for a hackathon project. Many ATS providers have a public API that you can hit with the slug of the companies to get open jobs. The hardest part was finding the companies. I tried Firecrawl but it returned around 200 companies per provider which wasn’t enough for me. Then, I tried SERPAPI but it was expensive. I ended up using SearXNG to discover companies by ATS type and fetch their job postings. This produced a large dataset of 200k+ jobs (I only use a subset as it would have taken too much time processing). A few days ago, I decided to build a visualization of the data as I didn’t know what to do with it and wanted people to benefit. I kept catching myself wanting to ask simple questions like “show only research roles in Europe” or “filter for remote SWE positions” (and had plenty of free ai credits) so I added a small LLM interface that translates natural language into filters on the map. The map is built with Vite + React + Mapbox. Live demo: https://map.stapply.ai GitHub (data): https://github.com/stapply-ai/jobs Would love feedback, ideas for improvement, or contributions.

Visit product View on Hacker NewsNov 24, 2025

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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Product HuntOn track for Day 1 leaderboard · Strong signals: visual, using, open · Missing: mac, agents, macos

65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

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