About the role
SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today, SpaceX is actively developing the technologies to make this possible, with the ultimate goal of making life multiplanetary and expanding consciousness to the stars.
SpaceXSI is building Super Intelligence to help humanity understand the universe. Our teams are building the full Super Intelligence stack: from our Colossus supercomputers, to Grok models, to our suite of applications, including Grok Bot, Grok Build, Grok Imagine, Grok Voice, and X. We're creating a powerful flywheel where more compute trains better models, better models power more useful products, and what we learn from people using those products shapes what we build next.
SOFTWARE ENGINEER, PRETRAINING DATA
We're looking for Software Engineers to build the data systems behind our frontier 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.
RESPONSIBILITIES
Data Platform
- 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
Data Quality
- 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