Research Crawling Engineer

Who We Are: We build infrastructure that delivers massive amounts of web data to the companies training the world’s most powerful AI models. We're the team that helps to power and support Grass, a bandwidth-sharing network that lets us operate a massive distributed crawler, giving us unique access to high-quality public web data at global scale. On top of that, we’ve built pipelines for ingesting, segmenting, and annotating billions of videos, transcripts, and audio files, powering dataset creation for frontier labs. We’re lean, technical, and move fast. No red tape, no slow decision-making; just a team of builders pushing to expand what’s possible for open web data and AI. Overview: As a Research Crawling Engineer, you will design and operate large-scale web data acquisition systems for research and model development. You will work will span distributed systems, scraping infrastructure, and data pipelines. Tasks Responsibilities: Build and maintain large-scale web crawlers across diverse domains Design high-throughput, fault-tolerant systems for data collection (millions to billions of URLs/day) Handle anti-bot systems, rate limits, and dynamic/JS-heavy sites Develop pipelines for cleaning, deduplication, filtering, and normalisation Construct and maintain datasets for research and model training Monitor crawl performance, coverage, and data quality; iterate quickly Collaborate with research teams to align data collection with modeling needs Optimize infrastructure for cost, latency, and reliability Requirements Requirements: Strong programming experience in one or more of: Go, Rust, Python, Java, or C++ Experience building web crawlers or large-scale data pipelines Solid understanding of HTTP, networking, and browser behavior Familiarity with distributed systems and parallel processing Experience working with large datasets (TB–PB scale preferred) Ability to debug unstable or adversarial environments Preferred / Bonus: Experience with NLP pipelines or dataset cura