Evals Infrastructure Tech Lead / Manager

Anthropic•San Francisco, CA

18h•$500,000 - $850,000•Hybrid

About The Position

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.

Requirements

1+ years of management experience in a technical environment, particularly performance or distributed systems
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
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

Nice To Haves

Significant experience building and maintaining large-scale distributed systems
Passionate about system reliability and performance
Enjoy solving complex technical challenges at scale
Comfortable working with ambiguous requirements and evolving specifications
Take ownership of problems and drive solutions independently
Excited about contributing to the development of safe and ethical AI systems
Can balance technical excellence with practical delivery

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