We’re looking for Software Engineers to build the data systems behind our frontier coding models’ initial training. You’ll work on large-scale crawling, data platform, and pipeline infrastructure, turning raw dumps into the datasets our models train on, and making iteration with researchers fast and reliable
The Data Quality team owns the entire road between raw internet-scale data and the tokens that train frontier models. This team makes sure the right data, in the right form, hits the training clusters on time and at the quality bar required to push the scaling curve. This is done by building our own models, our own high-performance pipelines, and by running the experiments that prove the data is actually stellar
The Data Platform Team owns the infrastructure and pipelines that transform raw data dumps into training-ready datasets. This team improves the speed, reliability, and developer experience of our initial training data pipelines so researchers can quickly experiment with new data sources, quality filters, taxonomies, multimodal data, and data mixes that improve model performance
The Crawling team owns large-scale web crawling and parsing that feeds the top of the funnel for initial training. They discover, schedule, fetch, and parse public web content so high-quality documents become the raw scrapes that Data Quality and Data Platform turn into tokens and mixes. This is deep distributed systems work with real ownership: host coverage and prioritization, fetch success under antibot and trap content, HTML/document parsing quality, and reliability of the crawl infrastructure that must continuously supply every downstream data pipeline
On the Data Quality Team
Build and own high-throughput, fully telemetered data pipelines that process frontier-scale data with end-to-end traceability. If something breaks or drifts, your systems will tell us before the training run does
Train and ship models that classify, rank, filter, clean, and identify data at extreme throughput. These models have to be both accurate and fast enough to sit in the critical path without becoming the bottleneck
Design and run scaling-ladder experiments on data-mixture, repeatability, and quality depth that turn “this dataset feels good” into hard evidence the training team can trust
Partner tightly with Data Acquisition to hunt down missing or low-quality sources, and with the training teams to close the loop on what actually moves loss and downstream evals
Treat data quality as a systems problem and a research problem. You will write performance-critical code one week and design careful experiments the next
On the Data Platform Team
Build the platform that turns raw web, code, multimodal, and acquired data into training-ready datasets for frontier pretraining runs
Own the pipelines, orchestration, and tooling that make pretraining data iteration fast, reliable, observable, and reproducible at scale
Create clear signals for data quality, lineage, freshness, and pipeline health so researchers can trust what goes into each run
Partner with initial training, crawling, data quality, and acquisition teams to turn new data ideas into measurable improvements in loss, evals, and model capability
On the Crawling Team
Build and scale the web crawling systems that discover, schedule, fetch, and parse high-quality documents across the open web for initial training
Improve URL seeding, scoring, and fair host scheduling so crawl capacity lands on the hosts and pages that matter most for model quality
Raise crawl success and parsing quality — defeating antibot failures, improving extractors, and capturing content we previously could not get cleanly
Debug and harden complex crawl infrastructure end-to-end for availability, recovery, and ingestion lag, and automate delivery of crawl datasets into the data pipeline
Work independently (and alongside AI agents) and partner with Data Quality and Data Platform so new coverage shows up as better tokens in training runs