Data Infra Engineer, Pretraining

Key Skills

Aartificial intelligence, Data Engineering, Infrastructure as code, Machine learning techniques, MLOps tools, Python Programming

Job Description

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 Research Engineer to join our Pretraining 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.

Key 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

Qualifications

• Strong software engineering skills with experience in building distributed systems
• Expertise in Python and experience with distributed computing frameworks
• 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

Preferred Experience

• Advanced degree (MS or PhD) 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

You'll thrive in this role if you

• 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

At Anthropic, we are committed to fostering a diverse and inclusive workplace. We strongly encourage applications from candidates of all backgrounds, including those from underrepresented groups in tech.

If you're excited about pushing the boundaries of AI while prioritizing safety and ethics, we want to hear from you!

The expected salary range for this position is:

Annual Salary:

$315,000—$340,000 USD

Logistics

Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.

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 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.