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

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

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

Sr. Software Engineer, Pretraining Data

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Preferred

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