The opportunity
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.
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.
What they're looking for
- 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.
