Staff+ Software Engineer, Account Compromise

Anthropic Anthropic · AI Frontier · London, United Kingdom · AI Research & Engineering

Staff+ Software Engineer focused on account compromise detection, response, and remediation within Anthropic's Safeguards organization. This role involves setting technical direction, owning architecture, threat modeling, leading investigations, and building systems to protect users from account takeover, credential abuse, and compromised API keys. The position requires strong software engineering fundamentals, experience with security systems, and the ability to make critical architectural decisions in an adversarial domain.

What you'd actually do

  1. Set the technical direction and own the architecture for account compromise detection, response, and remediation across Claude and the Claude Developer Platform
  2. Independently scope and lead complex, multi-month engineering projects from an ambiguous starting point through to production systems that operate reliably under adversarial pressure
  3. Build and evolve detection systems that identify account takeover, credential abuse, and compromised API keys in near real time
  4. Design automated response flows that cut off attacker access while minimising disruption to legitimate users
  5. Lead investigations into significant compromise incidents end to end, then convert what you learn into durable, automated defences

Skills

Required

  • Python
  • SQL
  • Software engineering fundamentals
  • Hands-on coding ability
  • Designing, building, and operating detection, anti-fraud, anti-abuse, or security systems
  • Scoping and delivering complex, ambiguous, multi-month technical projects
  • Making architectural decisions in an adversarial domain
  • Leading investigations into account-based abuse or security incidents
  • Translating findings into automated detection
  • Reasoning about large behavioural or telemetry datasets
  • Written communication
  • Driving alignment across multiple teams and stakeholders

Nice to have

  • Trust and safety
  • Platform integrity
  • Fraud
  • Detection and response
  • Technical lead or mentor experience
  • Account attack techniques
  • Authentication and identity systems (OAuth, SSO, MFA, device binding, risk-based authentication)
  • Applying machine learning to fraud or abuse detection
  • Cloud data tooling (BigQuery, Spark, dbt, Airflow)
  • Building tooling for operational or investigative teams
  • Partnering closely with users of tooling
  • AI safety
  • AI model misuse

What the JD emphasized

  • 10 + years experience designing, building, and operating detection, anti-fraud, anti-abuse, or security systems in production
  • track record of independently scoping and delivering complex, ambiguous, multi-month technical projects
  • Experience making architectural decisions in an adversarial domain that other engineers and teams then build on
  • Ability to reason rigorously about large behavioural or telemetry datasets, and to distinguish attacker behaviour from unusual but legitimate use
  • Sound judgement about the tradeoff between stopping bad actors and disrupting legitimate users, and the ability to explain and defend where you have drawn that line