Anthropic’s Staff+ Software Engineer, Safeguards Evals role focuses on building the evaluation infrastructure that informs safety system enforcement. The position sits at the intersection of applied machine learning research and engineering, designing experiments to measure investigative agent performance, constructing real-world misuse datasets, and shipping methods into production pipelines that gate every change to the system. The engineer will directly impact how much trust Anthropic places in its automated abuse detection and guide where resources are invested to improve it.
Staff+ Software Engineer, Safeguards Evals at Anthropic
Hybrid - San Francisco, CA
More jobs at AnthropicSalary
USD 320,000 - 485,000
Requirements
Skills
- Proficiency in Python and comfort working across the stack
- Experience building and maintaining data pipelines
- Experience working with LLMs and understanding of agentic systems
- Strong data analysis skills
- Ability to move fluidly between research prototyping and production-quality code
- Ability to translate ambiguous problems into concrete, testable experiments
- 8+ years of industry software engineering experience
- Expertise in building or contributing to agent evaluation frameworks, benchmarks, or automated grading systems
- Extensive experience in trust and safety, content moderation, or abuse detection systems
- Experience in red teaming, adversarial testing, or jailbreak research on AI systems
- Experience with synthetic data generation or data augmentation
- Experience with distributed systems or large-scale data processing
- Experience with prompt engineering or building LLM-powered applications
Responsibilities
- Build and own the evaluation harness for an agentic investigation system
- Construct high-quality evaluation datasets representing real-world misuse across harm areas
- Measure agent performance end-to-end and drive improvement on hardest harm areas
- Analyze coverage to identify measurement gaps and evolve evaluations
- Productionize successful research into regression and release pipelines
- Build tooling that enables policy experts to author, run, and iterate on evaluations without engineering support
- Construct reinforcement learning environments to improve Claude’s safety investigation capabilities
Technologies
PythonLarge Language Models (LLMs)Reinforcement Learning (RL) environmentsSynthetic data generationDistributed systemsLarge-scale data processingPrompt engineering
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