Pre-training Data Infrastructure Engineer

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 a 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 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, * Designing and implementing distributed computing architecture for web-scale data processing
• Building scalable infrastructure for model training data preparation
• Developing fault-tolerant distributed processing systems
• Implementing new infrastructure components based on research requirements

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.

• 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, * 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, 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.

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues.

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., We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact - advancing our long-term goals of steerable, trustworthy AI - rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We’re an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.