Infrastructure Engineer, Pre-training

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role 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. 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: 5+ YOE outside of internships Strong software engineering skills with experience in building distributed systems Expertise in Python and Rust Hands-on experience with distributed computing frameworks, particularly Apache Spark  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 annual compensation range for this role is listed below.  For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $350,000 — $850,000 USD Logistics 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 Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship:  We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not