Pre-training Data Infrastructure Engineer

About the role

About Anthropic

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

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 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

You may be a good fit if you have:

- 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

Strong candidates may have:

- 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

Sample Projects

- Designing and implementing distributed computing architecture for web-scale data processing