RemoteHQ

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  RemoteHQ Screenshot

Inspiration

Remote job search is full of noise. Platforms like LinkedIn and Indeed have useful listings, but they also contain stale posts, promoted roles, duplicates, misleading “remote” jobs, and scam-like opportunities. Job APIs are expensive and often still include the same low-quality aggregated data. We wanted to build a better source of truth by going directly to company career pages and owning the full job data pipeline ourselves.

What it does

RemoteHQ helps users find high-quality, legitimate remote jobs from verified companies. We scrape official company ATS pages, filter for remote roles, store the jobs in our own database, and use AI to extract useful signals like salary, experience level, role category, visa sponsorship, and remote-work details. The app then lets users search and filter across 17k+ scraped remote jobs from a curated pool of 1,700+ startups and tech companies.

How we built it

We built a Python scraping pipeline that reads verified companies from Supabase, queues scraping tasks with RabbitMQ, and fetches jobs directly from ATS providers like Greenhouse and Lever. Jobs are normalized, filtered for remote eligibility, and stored with history such as first seen, last seen, updated, and removed status. We then run an LLM over job descriptions to extract structured metadata. The frontend is a Next.js app connected to Supabase, with search, filters, pagination, and job cards linking back to the original company posting.

Challenges we ran into

The hardest part was building a data pipeline that was both reliable and useful. Different ATS providers structure job data differently, so we had to normalize listings into one consistent format. We also had to think carefully about quality: remote jobs are often mislabeled, job descriptions can be vague, and stale postings need to be tracked over time. Another challenge was keeping the system free and extensible instead of depending on paid job APIs.

Accomplishments that we're proud of

We’re proud that RemoteHQ is not just another job board UI. We built our own free pipeline, scraped from official company sources, and collected 17k+ remote jobs from 1,700+ high-quality companies. We also created the foundation for deeper job trust analysis because we own the data history. That means we can identify suspicious posting patterns, detect low-quality companies, and improve the quality of the dataset over time.

What we learned

We learned that job quality starts with data quality. If the source is noisy, the product will be noisy no matter how nice the UI is. We also learned that owning the pipeline gives us much more control: we can track freshness, understand company behavior, enrich descriptions with AI, and build trust signals that would be difficult with third-party job APIs.

What's next for RemoteHQ

Next, we want to add a legitimacy score for each job, company trust profiles, suspicious description detection, duplicate/repost detection, and alerts for newly posted high-quality remote roles. We also want to support more ATS providers beyond Greenhouse and Lever, and run scheduled quality analysis to automatically remove suspicious companies or low-quality postings from the platform.

Built With

• data
• next
• scraping
• vercel