Cursor Software Engineer, Pretraining Data building large-scale data systems for frontier AI model training. Develop pipelines, data quality models, and web crawling infrastructure.
Responsibilities
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
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
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
Qualification
ADDITIONAL REQUIREMENTSOnsite role
Required
Minimum of 3 years of software engineering experience
Software engineering experience building distributed systems and/or large-scale data infrastructure
Preferred
High-slope engineer who has moved unusually fast (e.g., staff-level ownership within a few years) or brings relevant domain experience
Able to architect and ship end-to-end with high ownership, debug complex systems independently, and work alongside AI agents
Strong intuitions about large-scale distributed systems
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
ADDITIONAL REQUIREMENTS:
Onsite role: must be able to work in person at the Palo Alto office
