Drimva

Anthropic

Cyber Evaluations Engineer

$300K–$405K

Remote-Friendly, United States +3 · Remote · Full-time · Posted Sep 1, 2026

Location
Remote-Friendly, United States
San Francisco, CA
Washington, DC
San Francisco, California, United States
Experience
Years of experience required will correlate with the internal job level requirements for the position.
Education
Bachelor’s degree or an equivalent combination of education, training, and/or experience; relevant field of study demonstrated through coursework, training, or professional experience.
Visa sponsorship
Visa sponsorship is available, but not for every role or candidate; if an offer is made, Anthropic will make every reasonable effort to obtain a visa.

About the job

Build and run evaluations of cyber capabilities and safeguard robustness in AI models, and develop probes and detection systems for cyber misuse.

What you'll do

  • Design and run cyber capability, uplift, and safety evaluations; perform robustness testing before major releases.
  • Analyze evaluation results and communicate findings to teams and stakeholders.
  • Build evaluation tooling and cyber-misuse detection probes, collaborating with policy and engineering teams on safeguards.

What they ask for

  • Experience building or running evaluations, benchmarks, or test suites for software or ML systems, including delivering results on short, fixed timelines.
  • Hands-on cybersecurity experience, such as CTF participation, vulnerability research, exploit development, or security research.
  • Proficiency in Python.
  • Strong ability to communicate evaluation results with cross-functional or policy stakeholders.

Nice to have

  • Deep offensive-security or security-research experience, including building AI security benchmarks.
  • Experience analyzing adversarial or abuse data, AI/ML evaluation frameworks, or detection content.

Pay

$300,000 — $405,000 USD (base salary)

Tools

Python, Sigma, YARA, Suricata, SIEM

AI focus

Evaluate cyber-relevant capabilities and safeguard robustness in models; develop abuse-detection probes and architecture.

Model-written summary · the original posting is authoritative

First seen Oct 8, 2026 · Last verified Nov 5, 2026 (UTC)