Research Engineer, Frontier Red Team (RSP Evaluations)

Details

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the Role

We are the team behind the Responsible Scaling Policy (RSP) Evaluations. We build and operate the automated systems that test whether frontier AI models are safe to release. Our evaluations determine if models have crossed critical capability thresholds in domains like autonomous replication, cybersecurity, and biological research. This is a production engineering role focused on building reliable evaluation infrastructure for AI safety. You'll create automated systems that systematically test frontier AI models for dangerous capabilities, working closely with domain experts to translate safety concerns into robust, scalable evaluation pipelines.

What makes this role unique

• High-stakes execution: Your systems directly determine whether cutting-edge AI models get released
• Model launch cadence: You'll build during development cycles and execute flawlessly during critical launch windows
• Production reliability: Build evaluation infrastructure that works consistently under pressure
• Cross-domain collaboration: Work with biosecurity, cybersecurity, and other domain experts to automate their safety assessments

Responsibilities

• Build and maintain automated evaluation systems using distributed infrastructure
• Create robust evaluation pipelines that can run thousands of model capability tests
• Develop tools that allow domain experts to quickly deploy new safety evaluations
• Ensure evaluation systems run reliably during high-stakes model launches
• Write production-quality Python code for evaluation infrastructure that scales
• Monitor and operate evaluation systems during critical assessment periods
• Collaborate with domain experts to translate safety requirements into technical implementations

You may be a good fit if you

• Are a reliable engineer who builds systems that work consistently under pressure
• Have strong Python programming skills and write clean, maintainable code
• Are comfortable working with LLMs programmatically (APIs, prompting, output processing)
• Have experience with debugging complex systems and resolving issues quickly
• Can work independently on 1-3 month projects while collaborating effectively with domain experts
• Understand systems optimization and can build efficient, scalable solutions
• Care about AI safety and want to contribute to responsible model development
• Thrive in environments that switch between building and execution modes

Strong candidates may also have

• Experience with evaluation frameworks, testing infrastructure, or automated assessment systems
• Background in physics, systems engineering, or other fields requiring critical thinking about experimental results
• Familiarity with distributed systems, containerization, or production operations
• Experience with adversarial testing, red-teaming, or finding edge cases in complex systems
• Understanding of LLM capabilities and limitations

What makes this role exciting

• Direct impact: Your systems determine whether the world's most advanced AI models get released
• Technical growth: Build expertise in AI safety evaluation while working with cutting-edge models
• Collaborative environment: Work with world-class domain experts across multiple safety-critical fields
• Production ownership: Own the full lifecycle from building evaluation systems to operating them during launches
• Mission-driven work: Contribute directly to ensuring AI systems remain safe and beneficial

Representative Projects

• Build automated red-teaming systems that generate and evaluate thousands of adversarial prompts
• Create evaluation pipelines that systematically test model capabilities across multiple risk domains
• Develop monitoring infrastructure that tracks evaluation results and detects capability jumps
• Implement reliable containerized environments for running large-scale model assessments
• Build tools that allow biosecurity experts to quickly create and deploy new biological risk evaluations
• Create automated analysis systems that process evaluation results and generate capability reports

Candidates need not have

• Previous AI/ML research experience
• Domain expertise in specific risk areas like biosecurity or cybersecurity
• Senior-level experience - we're looking for someone ready to grow into owning critical infrastructure

Deadline to apply: None. Applications will be reviewed on a rolling basis.