Data Infrastructure Engineer, Pre-training at Anthropic

Hybrid - San Francisco, CA, United States

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Anthropic is at the forefront of AI research, dedicated to developing safe, ethical, and powerful artificial intelligence. Our mission is to ensure that transformative AI systems are aligned with human interests. We are seeking a Staff level Engineer to join our Pre-training team, responsible for developing the next generation of large language models. In this role, you will work at the intersection of cutting-edge research and practical engineering, contributing to the development of safe, steerable, and trustworthy AI systems.

Salary

USD 500,000 - 850,000

Requirements

Skills

  • 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
  • Advanced degree in Computer Science or related field
  • Experience with language model training infrastructure
  • Background in Data Infrastructure, MLOps, or ML infrastructure
  • Significant experience building high-throughput fault-tolerant distributed systems
  • Expertise with Python and Rust
  • Passionate about system reliability and performance
  • Comfortable working with ambiguous requirements and evolving specifications
  • Take ownership of problems and drive solutions independently
  • Eager to learn about machine learning research and its infrastructure requirements

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

Technologies

Apache SparkPythonRustdistributed computing frameworks

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