About the job
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.
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
Minimum
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
Preferred
Have significant experience building high-throughput fault-tolerant distributed systems
Expertise with Python and Rust
Passionate about system reliability and performance
Are comfortable working with ambiguous requirements and evolving specifications
Take ownership of problems and drive solutions independently
Are excited about contributing to the development of safe and ethical AI systems
Can balance technical excellence with practical delivery
Are eager to learn about machine learning research and its infrastructure requirements