Security Risk Analyst, Risk Engineering

Location: San Francisco, CA | New York City, NY About Anthropic 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 The Security Risk team is responsible for how Anthropic identifies, prioritizes, and drives treatment of its most important security risks. We are rebuilding risk management to operate as an engineering function, using automation, quantitative risk and AI-native platforms to enable decision making. The risks we assess span Anthropic's full security landscape, so the team needs breadth across domains and the judgment to go deep wherever a decision demands it. Security Risk, in deep partnership with Security Engineering, helps define the security program and shapes how investment and treatment decisions get made. The conventional GRC playbook was not built for a company shipping frontier AI. You will help define what replaces it, with a direct line to CISO-level decisions. As a Security Risk Analyst you will take risk questions from leadership and partner teams and drive them to a decision. Sometimes that is a fast, structured qualitative assessment and other times it is a deep FAIR-based quantitative analysis. Either way you bring leadership a clear position, the tradeoffs, and honest uncertainty, and you defend it under challenge. In parallel, you will help turn quantitative risk into a product partner teams can leverage, and define the standards a growing risk function will run on. Key responsibilities Enable leadership and partner teams to make risk-informed decisions. Take ambiguous risk questions, drive them to a documented decision, and communicate the quantitative and qualitative tradeoffs clearly Lead quantitative analysis of the company's top security risk scenarios using FAIR, calibrated estimation, and simulation, working with the engineers who own the systems and presenting results in terms leadership can act on Partner with Security Engineering to assess threat scenarios and control effectiveness so security investment is right-sized to actual risk and remediation is sequenced where it buys down the most risk Frame escalations and risk treatment decisions with a clear recommendation, an honest statement of uncertainty, and re-evaluation triggers, and pressure test those decisions with risk owners Shape the analysis and narrative behind leadership risk reviews, and help define risk appetite and the risk metrics the organization should measure and why Use AI and automation to scale risk analysis, and own the calibration and quality review that keep the outputs trustworthy Turn one-off analyses into reusable methods, templates, and training so risk analysis becomes increasingly self service for partner teams, and raise the analytical quality of the risk register You may be a good fit if you Have owned security or technology risk analysis end to end, qualitative and quantitative, and can point to a decision your output changed, whether a funding call, a launch call, a remediation sequence, or a documented acceptance Have hands-on FAIR-style quantification experience, including scenario decomposition, calibrated estimation, and Monte Carlo simulation in Python, R, or spreadsheet tooling, briefed to decision makers Thrive in ambiguity. You frame a moving question into something analyzable, pick the depth that fits, and drive to a decision rather than a report Put defensible severity and likelihood on ambiguous problems, state uncertainty honestly, and change your position when the evidence changes Have enough technical security depth to decompose an attack path with security engineers and be credible in the room Can compress a complex risk position into one paragraph for the CISO, make the tradeoff explicit, and defend it under pointed questions Use Claude or other LLMs as daily working tools and review model output critically Are low ego and collaborative, build credibility through the work, and care about AI safety and the role security risk plays in it Strong candidates may also Have applied FAIR-CAM or another structured approach to control effectiveness and attack path modeling Have built or operated a cyber risk quantification program, or turned quantitative analysis into tooling, templates, or training that others ran Have helped define risk appetite or tolerance thresholds an organization actually used to make decisions Bring a background in security engineering, detection, threat intelligence, actuarial science, or decision science that grounds the numbers in real systems Are familiar with security risks specific to AI systems, such as goal or intent modification, and how they change traditional threat models The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $270,000 — $345,000 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 encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. 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. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.