Infrastructure Engineer (Pre-training)

• 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 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
• 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
• 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

Benefits

• Comprehensive health, dental, and vision insurance for you and your dependents
• Inclusive fertility benefits via Carrot Fertility
• 22 weeks of paid parental leave
• Flexible paid time off and absence policies
• Mental health support for you and your dependents
• Competitive salary and equity packages
• Optional equity donation matching at a 1:1 ratio, up to 25% of your equity grant
• Retirement plans with competitive matching
• Life and income protection plans
• $500/month flexible wellness and time saver stipend
• Commuter benefits
• Annual education stipend
• Home office stipends
• Relocation support for those moving for Anthropic
• Daily meals and snacks in the office- Strong background in performance optimization and system scaling
• Advanced degree in Computer Science or related field
• Strong background in distributed systems and parallel computing
• Expertise in tokenization algorithms and techniques
• Expertise in Python and Rust
• Experience building high-throughput, fault-tolerant systems
• Hands-on experience with distributed computing frameworks, particularly Apache Spark
• Background in MLOps or ML infrastructure
• Experience with language model training infrastructure
• Experience with infrastructure-as-code and configuration management
• Deep knowledge of monitoring and observability practices
• Strong software engineering skills with experience in building distributed systems
• 5+ YOE outside of internships
• Strong communication skills and ability to work in a collaborative environment
• Deep understanding of cloud computing platforms and distributed systems architecture
• Excellent problem-solving skills and attention to detail
• Experience with high-throughput, fault-tolerant system design
• Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience
• Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience
• We encourage you to apply even if you do not believe you meet every single qualification
• 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