Staff Data Engineer, Pre-training

Anthropic•San Francisco, CA

50d•$340,000 - $425,000•Hybrid

About The Position

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/TLM to join our Pre-training Data 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.

Requirements

7+ YOE outside of internships
Strong software engineering skills with experience in building distributed systems
Expertise in Python
Hands-on experience with distributed computing frameworks, particularly Apache Spark is a must
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

Nice To Haves

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

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