Infrastructure Engineer, Pre-training

About the Role

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 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 high-performance data processing infrastructure for large language model training
• 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
• Implement comprehensive monitoring systems for data processing infrastructure
• Create and optimize distributed computing systems for processing web-scale datasets
• Collaborate with research teams to implement novel data processing architectures
• Build and maintain documentation for infrastructure components and systems
• Design and implement systems for reproducibility and traceability in data preparation

You may be a good fit if you have:

• 5+ YOE outside of internships
• Strong software engineering skills with experience in building distributed systems
• Expertise in Python and Rust
• Hands-on experience with distributed computing frameworks, particularly Apache Spark 
• Deep understanding of cloud computing platforms and distributed systems architecture
• Experience with high-throughput, fault-tolerant system design
• Strong background in performance optimization and system scaling
• 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
• Strong background in distributed systems and parallel computing
• Expertise in tokenization algorithms and techniques
• Experience building high-throughput, fault-tolerant systems
• Deep knowledge of monitoring and observability practices
• Experience with infrastructure-as-code and configuration management
• Background in MLOps or ML infrastructure

Strong candidates may have:

• Have significant experience building and maintaining large-scale distributed systems
• Are passionate about system reliability and performance
• Enjoy solving complex technical challenges at scale
• 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

Sample Projects

• Designing and implementing distributed computing architecture for web-scale data processing
• Building scalable infrastructure for model training data preparation
• Creating comprehensive monitoring and alerting systems
• Optimizing tokenization infrastructure for improved throughput