Role overview

Staff Engineer Security

Requirements and responsibilities

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Responsibilities

  • Identify, assess, and mitigate AI-specific security risks, including model poisoning, adversarial attacks, prompt injection, model inversion, data leakage, and supply chain vulnerabilities.
  • Conduct threat modeling and security architecture reviews for AI/ML systems, APIs, and third-party AI services.
  • Define and operationalize AI security standards, controls, and guardrails aligned with industry frameworks (e.g., NIST AI RMF, OWASP Top 10 for LLMs).
  • Support development and enforcement of AI governance policies, risk management frameworks, and compliance requirements.
  • Partner with engineering, data science, and product teams to embed security controls into AI systems throughout the development lifecycle.
  • Evaluate and govern third-party AI vendors, platforms, and open-source models.
  • Provide subject matter expertise and mentorship to security engineers, ML engineers, and product teams.
  • Influence secure AI practices and drive adoption of best practices across the organization.
  • Translate AI security risks into business impact and communicate effectively with senior leadership.
  • Support strategic decision-making by providing risk-based recommendations and trade-off analysis.
  • Stay current on emerging AI threats, vulnerabilities, and defense techniques.
  • Contribute to long-term AI security strategy, roadmap development, and organizational readiness.

Qualifications

  • Bachelor's degree in computer science, Information Security, Engineering, or a related field (or equivalent practical experience).
  • 10+ years of experience in application security, product security, or security engineering.
  • Direct experience securing AI/ML systems, LLM-based applications, or data science platforms.
  • Familiarity with AI security frameworks (e.g., NIST AI RMF, OWASP Top 10 for LLMs).
  • Hands-on experience with secure SDLC practices (e.g., threat modeling, SAST, DAST, and penetration testing).
  • Strong understanding of AI/ML concepts and associated security risks.
  • Experience with cloud platforms (e.g., AWS, Azure) and modern development practices (CI/CD, DevSecOps).
  • Knowledge of privacy, regulatory, and compliance requirements applicable to AI systems (e.g., HIPAA, SOC2, HITRUST).
  • Experience building or deploying security tooling for AI platforms.
  • Experience translating technical risks into business context and influencing stakeholders.
  • Excellent communication, collaboration, and problem-solving skills.
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Browse stack
FocusSecurity EngineeringRole area
Seniority signalSeniorCandidate level
StackAWS, Azure, CI/CDPrimary skills
Location3 accepted countriesEligibility

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