Data Infrastructure Engineer, Pre-training

Responsibilities

• Design and implement highly performant, reproducible, and traceable data-processing infrastructure for large language model training.
• Develop scalable processing primitives including tokenization, deduplication, and chunking.
• Build data quality assurance and validation systems at scale.
• Collaborate with research teams to implement novel data-processing architectures.
• Build and operate end-to-end pipelines that transform raw web-scale corpora into training-ready datasets.
• Develop distributed computing architectures and fault-tolerant infrastructure components based on research requirements.

Requirements

• At least 5 years of professional experience outside internships.
• Strong software engineering skills building high-throughput, fault-tolerant distributed systems.
• Hands-on experience with distributed computing frameworks, particularly Apache Spark.
• Advanced degree in Computer Science or a related field preferred.
• Experience with language model training infrastructure preferred.
• Background in data infrastructure, MLOps, or ML infrastructure preferred.
• Expertise with Python and Rust preferred.
• Strong problem-solving, communication, collaboration, ownership, and attention-to-detail skills.

Benefits

• Hybrid work policy requiring staff to be in an Anthropic office at least 25% of the time, with some roles requiring more office time.
• Visa sponsorship with immigration-lawyer support when sponsorship is possible.
• Competitive compensation and benefits.
• Optional equity donation matching.
• Generous vacation and parental leave.
• Flexible working hours.
• Collaborative office space.