Software Engineer, Pretraining

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About the role

Our mission is to automate coding. The first step in our journey is to build the best tool for professional programmers, using a combination of inventive research, design, and engineering. Our organization is very flat, and our team is small and talent dense. We particularly like people who are truth-seeking, passionate, and creative. We enjoy spirited debate, crazy ideas, and shipping code. ABOUT THE ROLE 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. WHAT YOU’LL DO - 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:

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