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Pre-training Data Infrastructure Engineer

Anthropic· AI Research & Engineering
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San Francisco, CA$500k - $850kfull-timePosted 11mo ago

About the role

Staff-level data infrastructure engineer on the Pre-training team focused on building the data processing infrastructure and pipelines for large language model training, with emphasis on scalability, reliability, and collaboration with research.

Responsibilities

  • Design and implement data processing infrastructure for large language model training (highly performant, reproducible, traceable)
  • Develop and maintain core processing primitives (e.g., tokenization, deduplication, chunking) with a focus on scalability
  • Build robust systems for data quality assurance and validation at scale
  • Collaborate with research teams to implement novel data processing architectures
  • Build and operate end-to-end data pipelines that turn raw web-scale corpora into training-ready datasets

Qualifications

  • 5+ YOE outside of internships
  • Strong software engineering skills with experience building high-throughput fault-tolerant distributed systems
  • Hands-on experience with distributed computing frameworks, particularly Apache Spark
  • Excellent problem-solving skills and attention to detail
  • Strong communication skills and ability to work in a collaborative environment
  • Advanced degree in Computer Science or related field
  • Experience with language model training infrastructure
  • Background in Data Infrastructure, MLOps, or ML infrastructure

Skills

Required:Distributed systemsApache SparkData processing pipelines
Nice to have:TokenizationPythonRust

Benefits

  • competitive compensation and benefits
  • optional equity donation matching
  • generous vacation and parental leave
  • flexible working hours
  • lovely office space
Last updated Sep 23, 2026. Interested? Apply on the company site.

About Anthropic

building reliable, interpretable, and steerable AI systems

Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.

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