AnthropicStaff Infrastructure Engineer, Pre-training
Seeking Staff Infrastructure Engineer to design and implement high-performance data processing infrastructure for large language model training, with 7+ years of experience and expertise in Python and distributed systems.
About Anthropic
Anthropic’s mission is to create reliable, interpretable, and steerable AI systems.
About the Role
Anthropic is at the forefront of AI research, dedicated to developing safe, ethical, and powerful artificial intelligence.
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:
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
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
- Developing fault-tolerant distributed processing systems
- Implementing new infrastructure components based on research requirements
- Building automated testing frameworks for distributed systems
The expected base compensation for this position is below.
Annual Salary:
$340,000—$425,000 USD
Logistics
Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.
Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate.
How we're different
We believe that the highest-impact AI research will be big science.