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Anthropic's Safeguards organization is seeking a Staff+ Software Engineer to set technical direction for the Account Compromise team. This role owns the architecture for detecting, containing, and remediating account compromise across Claude and the Claude Developer Platform.
You will be responsible for designing systems that protect users and organizations from account takeover, credential stuffing, phishing-driven session theft, leaked API keys, and resold access. The work spans detection systems that identify compromise in near real-time, automated response flows that cut off attacker access while minimizing disruption to legitimate users, and end-to-end incident investigation and remediation.
Key responsibilities include: setting technical direction and owning architecture for account compromise detection and response; independently scoping and leading complex multi-month engineering projects from ambiguous starting points; building detection systems for account takeover and credential abuse; designing automated response flows; leading significant compromise incident investigations; threat modeling attacker adaptation; driving cross-organizational alignment with Security, Product, Support, and Policy teams; defining success metrics; setting technical standards and mentoring other engineers; and surfacing patterns to research and product teams.
This is a high-autonomy role operating in an adversarial domain. You will make judgment calls about where to draw lines between stopping bad actors and disrupting legitimate users, and you will need to defend those decisions rigorously.
Minimum qualifications: 10+ years designing, building, and operating detection, anti-fraud, anti-abuse, or security systems in production; track record of independently scoping and delivering complex multi-month technical projects; experience making architectural decisions in adversarial domains; proficiency in Python and SQL with strong software engineering fundamentals; experience leading investigations into account-based abuse and translating findings into automated detection; ability to reason rigorously about behavioral and telemetry datasets; strong written communication and cross-team alignment skills; sound judgment about security/usability tradeoffs.
Preferred: significant trust and safety, platform integrity, or fraud engineering experience including technical leadership; deep familiarity with account attack techniques; experience with authentication systems (OAuth, SSO, MFA, device binding, risk-based auth); experience applying ML to fraud/abuse detection; experience with cloud data tooling.
