Software Engineer

InfoHawk · Cybersecurity

unknownRemote

Salary Range (USD)

Negotiable

Location

London or NYC, UK or USA

Visa Support

Not Supported

Funding Stage

Pre-seed

Job Responsibilities

  • Own data ingestion pipelines including scrapers and collectors
  • Maintain integration with official APIs and undocumented endpoints
  • Detect and adapt to source changes using AI
  • Collaborate directly with founders

Required Skills

3-5 years building scraping or integration systems in productionExperience with TypeScriptExperience with Node.jsScraping and data collection expertise

Engineering Culture & Tech Stack

TypeScriptNode.jsSvelteKitPostgreSQLPlaywrightAI
ownership
remote-first

Raw Post

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InfoHawk | Software Engineer | London, UK or NYC | REMOTE (UK or US), occasional onsite | Full-time | https://www.infohawk.com Fraud reaches people through ads, cloned brand sites, counterfeit shops and AI-generated video, and it moves faster than any manual review. InfoHawk detects it while it is still running, correlates it across platforms and formats, and surfaces it early enough for our customers to act. We are a small team of engineers plus security, privacy and fraud specialists. Pre-seed, $2.25M led by Moonshots Capital: https://www.infohawk.com/news/2026-06-08-pre-seed-funding-an... You’d own how data gets in: scrapers and collectors that survive source changes, official APIs where they exist, undocumented endpoints where they don’t. Sources break constantly. Can you figure out how to use AI to spot the change and adapt automatically? Stack: TypeScript, Node, SvelteKit, Postgres, Playwright, heavy AI-assisted development. We’re looking for ~3-5 years building scraping or integration systems in production, but if you’re a great engineer who doesn’t tick every box we’d still like to hear from you. UK-based for London, US-based for NYC; we cannot sponsor visas. Two of our three founders are engineers, one in each city, and you’ll work with them directly. james@infohawk.com

AI Risk Insights

No major risk signals detected.

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Data Source

Content parsed by LLM from Hacker News raw data. Confidence:HIGH

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