Research Engineer, Model Evaluations

Anthropic•San Francisco, CA

52d•$300,000 - $405,000•Hybrid

About The Position

As a Research Engineer on the Model Evaluations team, you'll lead the design and implementation of Anthropic's evaluation platform-a critical system that shapes how we understand, measure, and improve our models' capabilities and safety. You'll work at the intersection of research and engineering to develop and implement model evaluations that give us insight into emerging capabilities and build robust evaluation infrastructure that directly influences our training decisions and model development roadmap. Your work will be essential to Anthropic's mission of building safe, beneficial AI systems. You'll collaborate closely with training teams, alignment researchers, and safety teams to ensure our models meet the highest standards before deployment. This is a technical leadership role where you'll drive both the strategic vision and hands-on implementation of our evaluation systems.

Requirements

Have experience designing and implementing evaluation systems for machine learning models, particularly large language models
Have demonstrated technical leadership experience, either formally or through leading complex technical projects
Are skilled at both systems engineering and experimental design, comfortable building infrastructure while maintaining scientific rigor
Have strong programming skills in Python and experience with distributed computing frameworks
Can translate between research needs and engineering constraints, finding pragmatic solutions to complex problems
Are results-oriented and thrive in fast-paced environments where priorities can shift based on research findings
Enjoy collaborative work and can effectively communicate technical concepts to diverse stakeholders
Care deeply about AI safety and the societal impacts of the systems we build
Have experience with statistical analysis and can draw meaningful conclusions from large-scale experimental data

Nice To Haves

Experience with evaluation during model training, particularly in production environments
Familiarity with safety evaluation frameworks and red teaming methodologies
Background in psychometrics, experimental psychology, or other fields focused on measurement and assessment
Experience with reinforcement learning evaluation or multi-agent systems
Contributions to open-source evaluation benchmarks or frameworks
Knowledge of prompt engineering and its role in evaluation design
Experience managing evaluation infrastructure at scale (thousands of experiments)
Published research in machine learning evaluation, benchmarking, or related areas

Responsibilities

Design novel evaluation methodologies to assess model capabilities across diverse domains including reasoning, safety, helpfulness, and harmlessness
Lead the design and architecture of Anthropic's evaluation platform, ensuring it scales with our rapidly evolving model capabilities and research needs
Implement and maintain high-throughput evaluation pipelines that run during production training, providing real-time insights to guide training decisions
Analyze evaluation results to identify patterns, failure modes, and opportunities for model improvement, translating complex findings into actionable insights
Partner with research teams to develop domain-specific evaluations that probe for emerging capabilities and potential risks
Build infrastructure to enable rapid iteration on evaluation design, supporting both automated and human-in-the-loop assessment approaches
Establish best practices and standards for evaluation development across the organization
Mentor team members and contribute to the growth of evaluation expertise at Anthropic
Coordinate evaluation efforts during critical training runs, ensuring comprehensive coverage and timely results
Contribute to research publications and external communications about evaluation methodologies and findings