Evals Infrastructure Tech Lead / Manager at Anthropic — San Francisco, CA

## About Anthropic

## 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're looking for an experienced tech lead to join our Evals Infrastructure team, building the systems that let us measure what our models can actually do. Evaluation is how we know whether a model is safe to ship — you'd own the infrastructure that makes those measurements fast, reliable, and trustworthy at scale. In this role you'll work at the intersection of inference, research and infrastructure engineering: managing the large scale distributed systems that orchestrate evals for our frontier models, building and scaling the harnesses researchers use to design and run evals, making results reproducible and interpretable, and ensuring eval signal is available where decisions get made. Your work directly shapes what we build and what we don't.

## 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

• Provide front-line leadership of engineering efforts to improve model performance and scale our inference and training systems

• Become familiar with the team’s technical stack enough to make targeted contributions as an individual contributor

• Manage day-to-day execution of the team's work

• Prioritize the team’s work and manage projects in a highly dynamic, fast paced environment

• Coach and support your reports in understanding, and pursuing, their professional growth

• Maintain a deep understanding of the team's technical work and its implications for AI safety

## You may be a good fit if you have

• Have 1+ years of management experience in a technical environment, particularly performance or distributed systems
• Are deeply interested in the potential transformative effects of advanced AI systems and are committed to ensuring their safe development
• Excel at building strong relationships with stakeholders at all levels
• Have experience managing teams through periods of rapid growth and change
• Strong software engineering skills with experience in building distributed systems
• Expertise in Python and Rust
• 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
• 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

## 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

### 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

Annual Salary:

$500,000—$850,000 USD

## Logistics

## How we're different

## Come work with us!