Security Risk Analyst, Risk Engineering

San Francisco, CA | New York City, NY · San Francisco · USD 270,000-345,000 / year · Hybrid · Posted Oct 5, 2026

At a glance

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.

The Security Risk Analyst will help rebuild risk management as an engineering function at Anthropic, utilizing automation and quantitative analysis to inform security decisions. This role involves partnering with Security Engineering to assess threat scenarios and define the security program for frontier AI systems.

Enable leadership and partner teams to make risk-informed decisions by driving ambiguous risk questions to documented conclusions.
Lead quantitative analysis of top security risk scenarios using FAIR, calibrated estimation, and simulation.
Partner with Security Engineering to assess threat scenarios and control effectiveness to right-size security investments.
Frame escalations and risk treatment decisions with clear recommendations, uncertainty statements, and re-evaluation triggers.
Shape the analysis and narrative for leadership risk reviews and define risk appetite and metrics.
Use AI and automation to scale risk analysis while owning calibration and quality reviews.
Develop reusable methods, templates, and training to make risk analysis self-service for partner teams.

Experience owning security or technology risk analysis end-to-end, including qualitative and quantitative methods.
Hands-on FAIR-style quantification experience, including scenario decomposition, calibrated estimation, and Monte Carlo simulation.
Proficiency in Python, R, or spreadsheet tooling for risk analysis.
Technical security depth sufficient to decompose attack paths with security engineers.
Ability to communicate complex risk positions and tradeoffs clearly to leadership.
Experience using LLMs as daily working tools with critical review of model output.
Bachelor’s degree or an equivalent combination of education, training, and/or experience in a relevant field.

Applied FAIR-CAM or other structured approaches to control effectiveness and attack path modeling.
Experience building or operating a cyber risk quantification program.
Experience turning quantitative analysis into tooling, templates, or training.
Experience defining risk appetite or tolerance thresholds used for organizational decision-making.
Background in security engineering, detection, threat intelligence, actuarial science, or decision science.
Familiarity with security risks specific to AI systems, such as goal or intent modification.

Location-based hybrid policy: staff expected to be in one of the offices at least 25% of the time.
Some roles may require more time in the office.