Sr. Software Engineer, Pretraining Data

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.

BASIC QUALIFICATIONS:

• 5+ years of professional software engineering experience building distributed systems and/or large-scale data infrastructure

PREFERRED SKILLS AND EXPERIENCE:

• Strong infrastructure or data platform background, ideally with a spike of outlier depth

• 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, including running them reliably at frontier scale

• Track record of leading technical projects across teams and mentoring other engineers

• 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

COMPENSATION AND BENEFITS:

The annual compensation range for this role is listed below.

Annual Salary:

$220,000 - $357,000 USD