Software Engineer, Pretraining Data

Job description

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. ## 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:** - 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. ## You may be a fit if - You have a strong infrastructure or data platform background, and ideally a spike of outlier depth somewhere (crawling/search infra is a plus, not a hard requirement) - You are a high-slope engineer who has moved unusually fast — for example, staff-level ownership within a few years — or you bring deep domain experience - You are able to architect and ship end-to-end with high ownership, debug complex systems independently, and work alongside AI agents - You have strong intuitions about large-scale distributed systems - You’re excited to learn how pre-training data shapes model quality, and want the ownership and visibility that comes with building systems that feed frontier training runs #LI-DNI