Roman Darker is an AI Data Specialist and software engineer with nine years of experience building production-ready web and AI tooling, from a GraphQL caching library published on npm to end-to-end SDK work for an LLM-focused web crawler. He blends backend engineering (Node.js, Rust SDKs, Express, Redis, Docker) with front-end UX improvements (React, Chart.js) and strong TypeScript practices to deliver measurable performance gains—his caching work cut retrieval times by up to 90%. At DataAnnotation he applies RLHF, prompt engineering, and rapid environment mocking to refine LLM behavior and make generated code runnable across languages. An active open-source contributor, he’s built full-stack apps (CarryOn, Quitr) that demonstrate practical security and scalability choices like JWT/Bcrypt and MongoDB sharding. Based in the NYC area with a BA from Rhodes College, he pairs an explorative, maker mindset with a knack for simplifying complex problems into compact developer tools.
9 years of coding experience
2 years of employment as a software developer
Wesleyan University
Bachelor of Arts - BA, Bachelor of Arts - BA at Rhodes College
🔥 Turn entire websites into LLM-ready markdown or structured data. Scrape, crawl and extract with a single API.
Role in this project:
Back-end Developer & QA Engineer
Contributions:2 reviews, 6 PRs, 19 comments in 3 months
Contributions summary:Roman focused on implementing a Rust SDK for interacting with the Firecrawl API, including methods for scraping, searching, and crawling. They also initiated end-to-end tests to validate the functionality of the SDK, demonstrating a strong understanding of testing methodologies. The user's work included setting up testing infrastructure and verifying the correct behavior of the API integration with the newly developed SDK. This involved creating test cases for scrape, crawl, and search functions.
Contributions:235 commits, 15 PRs, 457 pushes in 8 months
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