Data Infrastructure Engineer, Pre-training

The shape of it

In the posting’s own words

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.

What it asks for · 8

• 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

Also a plus

• 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

What the job covers

• 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

Degree language

• Advanced degree in Computer Science or related field

Tools and skills named

Words the posting leans on

• systems8×
• data7×
• infrastructure7×
• research5×
• experience4×
• data processing3×
• developing3×
• fault-tolerant distributed3×
• model training3×
• requirements3×
• skills3×
• architecture2×
• build2×
• building high-throughput2×
• contributing2×
• development safe2×

Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.

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