Staff+ Software Engineer, Safeguards Evals

Menlo Ventures; San Francisco (CA); On-site; USD 320,000 - 485,000; Full time

Benefits offered by this job

Flexible working hours

Generous vacation and parental leave

Office space for collaboration

Job summary

Menlo Ventures in San Francisco seeks an expert to build evaluation infrastructure for safety systems. This role involves designing experiments to measure an investigative agent's performance, building real datasets, and enhancing automated abuse detection systems.

The ideal candidate has a solid background in Python, LLMs, and data analysis, and they will produce high-quality datasets representing real-world misuse scenarios.

Qualifications

• Proficiency in Python and comfort working across the stack.
• Ability to translate ambiguous problems into concrete experiments.
• Experience working with LLMs and their failure modes.

Responsibilities

• Build and own evaluation harnesses for agentic investigation systems.
• Construct high-quality eval datasets representing real-world misuse.
• Analyze gaps in measurement and evolve evaluations over time.

Skills

Python proficiency

Data analysis

Experience with LLMs

Data pipeline management

Education

Bachelor’s degree or equivalent

Tools

Data augmentation techniques

Distributed systems

Job description

About the role

How do we know our safety systems actually catch misuse? Anthropic increasingly uses AI to investigate potential misuse of Claude — analyzing real-world traffic to surface bad actors, policy violations, and emerging threats. Its findings inform enforcement actions and model launch decisions, which means we need rigorous, trustworthy answers to questions like: Does the monitoring agent catch what it should? Where does it fail? Does it stay reliable as adversaries adapt, as models improve, and as the agent itself changes?

This role builds the evaluation infrastructure that answers those questions. You'll sit at the intersection of applied ML research and engineering — designing experiments to measure how well an investigative agent performs across harm areas, building datasets that represent real abuse rather than synthetic benchmarks, and shipping those methods into pipelines that gate every change to the system. Your work directly determines how much trust Anthropic can place in its automated abuse detection, and where we invest to make it better.

Key responsibilities

• Build and own the evaluation harness for an agentic investigation system — defining metrics, test cases and grading approaches for a complex long horizon agent
• Construct high-quality eval datasets representing real-world misuse across harm areas (e.g., cyber attacks, bio weapons, influence operations), drawing from real traffic patterns and synthetic generation
• Measure agent performance end-to-end (detection precision/recall, investigation quality, robustness) and drive hill-climbing on the hardest harm areas
• Analyze coverage to identify measurement gaps, and evolve evals so they remain unsaturated and high-signal as agent capabilities advance
• Productionize successful research into regression and release pipelines that run on every agent change, prompt update, and underlying model upgrade
• Build tooling that enables policy experts to author, run, and iterate on evaluations without engineering support
• Construct RL environments to improve Claude’s safety investigation capabilities.

Minimum qualifications

• Proficiency in Python and comfort working across the stack
• Experience building and maintaining data pipelines
• Experience working with LLMs and a working understanding of their capabilities and failure modes — especially agentic systems with tool use and multi-step reasoning
• Strong data analysis skills — you can draw reliable insights from large datasets
• Ability to move fluidly between research prototyping and production-quality code
• Ability to translate ambiguous problems into concrete, testable experiments

Preferred qualifications

• 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

The annual compensation range for this role is listed below.

Annual Salary: $320,000 — $485,000 USD

Logistics

• Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience
• Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience
• Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position
• Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
• Visa sponsorship: We do sponsor visas! However, we aren’t able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues.