Data Engineer

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.

Bachelor’s degree or equivalent work experience

Python (advanced) — strong grasp of async programming, multiprocessing, and writing production-grade code for long-running data jobs

Web scraping at scale — hands-on experience with high-volume scraping (proxies, rate limiting, anti-bot evasion). Experience with platform APIs and large media/metadata datasets (video platforms, social media)

Distributed data pipelines — experience designing and operating pipelines across many workers/servers using task queues (Celery, Kafka, RabbitMQ, or similar)

Data warehousing — practical experience with columnar/analytical warehouses; Databend, ClickHouse, or BigQuery strongly preferred; comfortable with complex analytical queries, partitioning strategies, cost-aware querying on cloud warehouses

Docker & Kubernetes — containerizing workloads, writing Helm charts/manifests, managing deployments, autoscaling scraping/processing workloads

Linux & bare-metal ops — comfortable managing services on Linux servers, debugging performance issues (disk I/O, network, memory) without managed-cloud abstractions

CI/CD for data workflows (GitHub Actions, ArgoCD)

Writing Scalable API