Research Engineer, Model Evaluations

Position Overview

At Anthropic, our mission is to create reliable, interpretable, and steerable AI systems that are safe and beneficial for both users and society. As a Research Engineer on the Model Evaluations team, you will drive the design and implementation of our evaluation platform – a critical system that shapes how we understand, measure, and improve our models’ capabilities and safety. In this role you will work at the intersection of research and engineering, developing novel evaluation methodologies, building high-throughput evaluation pipelines, and providing insights that directly influence training decisions and model development. You will collaborate with cross-functional teams including training, alignment, and safety groups to ensure our AI systems meet the highest standards before deployment.

Key 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

Required Qualifications

• Have experience designing and implementing evaluation systems for machine learning models, particularly large language models
• Demonstrated technical leadership experience, either formally or through leading complex technical projects
• Skilled at both systems engineering and experimental design, with the ability to build infrastructure while maintaining scientific rigor
• Strong programming skills in Python and familiarity with distributed computing frameworks
• Ability to translate between research needs and engineering constraints to find pragmatic solutions for complex challenges
• Results-oriented mindset with the capacity to thrive in fast-paced environments where priorities can shift
• Excellent collaborative and communication skills, with the ability to convey technical concepts to diverse stakeholders
• Deep commitment to AI safety and understanding of the societal impacts of AI systems
• Experience with statistical analysis and drawing meaningful conclusions from large-scale experimental data
• Education: At least a Bachelor's degree in a related field or equivalent experience

Preferred Qualifications

• 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 related 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

Benefits & Perks

• Salary: $300,000-$405,000 USD
• Compensation: Total package includes equity and potential incentive compensation for full-time employees
• Benefits: Competitive benefits including optional equity donation matching, generous vacation and parental leave, and flexible working hours
• Location: Hybrid policy with an expectation of at least 25% in-office presence, with some roles requiring additional time onsite
• Visa sponsorship may be available based on eligibility