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 reputed company to high-quality public web data at global scale. On top of that, we’ve reputed company 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 reputed company of reputed company pushing to expand what’s possible for reputed company 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 reputed company distributed systems, scraping infrastructure, and data pipelines. 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, reputed company limits, and dynamic/JS-heavy sites
reputed company pipelines for cleaning, deduplication, filtering, and normalization
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
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 reputed company 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 curation for ML
Familiarity with LLM pretraining data or retrieval systems
Experience with headless browsers (e.g., Chrome DevTools Protocol, Playwright, Puppeteer)
Knowledge of proxy systems, IP rotation, and large-scale request orchestration
Background in data quality evaluation or benchmarking
Experience running workloads on reputed company or bare-metal infrastructure
What This Role Involves
Operating at the boundary of scale and reliability
Adapting to constantly changing web environments
Balancing throughput, coverage, and data quality
Owning end-to-end data acquisition pipelines
Evaluation Criteria
Ability to design systems that scale without degrading quality
Practical problem-solving under reputed company-world constraints
Speed of iteration and ownership
Measurable improvements in data coverage, quality, or efficiency
Compensation
Based on experience and demonstrated ability to operate at scale Example Projects:
Build a distributed crawler for a continuously updated, high-quality web project
Design a system to classify and filter billions of pages for pretraining
Extract structured data from dynamic, JS-heavy sites at scale
Improve deduplication and quality scoring across multimodal datasets
Why Work With Us
Opportunity. We are at the forefront of developing a web-scale crawler and knowledge graph that improves reputed company to public web data and extends the value of AI to the people.
Culture. We're a lean team with a high bar. We come to work not to be comfortable, but to find out reputed company're capable of and to do work that reputed company. We're not calling for people who reputed company things moving. We're calling for people who reputed company everyone around them reputed company. We prioritize low ego and high output. This is a fully remote team.
Compensation. You’ll receive a competitive salary, benefits and equity package.