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Software Engineer, Pretraining Data

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Software Engineer, Pretraining Data is a still-open role at Cursor on Jobrow. Location: On-site · Palo Alto, CA · Palo Alto · California · United States. Sourced from the company's public Ashby board. First seen 2 months ago; last confirmed 9 days ago. Apply on the employer ATS — Jobrow is not the employer.

Pay
—
Workplace
On-site
Location
Palo Alto, CA · Palo Alto · California · United States
First seen
2 months ago
Last seen
9 days ago
Board
Ashby
Apply

About this 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.

  • Function: Engineering
  • Seniority: mid

Location & pay

On-site · Palo Alto, CA · Palo Alto · California · United States No pay range was listed on the public board feed.

Source

Jobrow reads the public Ashby board for Cursor. First seen 2 months ago; last seen 9 days ago. We are not the employer — apply only on the company ATS.

Posting

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 p

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