Sr. Software Engineer, Pretraining Data

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.

SR. SOFTWARE ENGINEER, PRETRAINING DATA

We're looking for Senior Software Engineers to build and own 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. As a senior engineer, you'll set technical direction for these systems, lead projects across teams, and mentor other engineers as our pretraining data scales.

RESPONSIBILITIES

• Data Platform: Design, build, and scale the distributed infrastructure that transforms raw web, code, multimodal, and acquired data into training-ready datasets for frontier model pretraining. Own high-throughput data pipelines, orchestration systems, and tooling that enable fast, reliable, observable, and reproducible data processing at massive scale, with end-to-end visibility into data lineage, freshness, and pipeline health.
• Data Quality: Build and deploy high-performance models and algorithms to classify, rank, deduplicate, filter, and clean training data at extreme throughput. Design and execute rigorous experiments across data mixtures, sampling strategies, and quality thresholds to quantify their impact on training loss, downstream evaluations, and model capabilities, translating research findings into production systems.
• Crawling: Build and optimize large-scale web crawling infrastructure spanning URL discovery, prioritization, host scheduling, fetching, parsing, and content extraction. Improve crawl coverage, efficiency, and reliability through intelligent scheduling, anti-bot resilience, robust extraction systems, and automated recovery, ensuring high-quality web data flows seamlessly into downstream training pipelines.
• Technical Leadership & Collaboration: Lead complex engineering initiatives across Data Platform, Data Quality, Crawling, Acquisition, and model training teams. Influence technical direction, drive architectural decisions, mentor engineers, and translate improvements in data infrastructure, coverage, and quality into measurable gains in model performance.