Plaid
Security Engineer - Platform Security
Seattle Office (Remote)$188.7k–$242.7kfulltimemidAdded today
About this role
Plaid seeks a Security Engineer to lead AI security efforts on the Platform Security team. You'll assess AI systems for vulnerabilities, design containment controls, manage AI identities, and detect prompt injection attacks to enable safe AI innovation at scale.
What you'll do
- Review AI systems and integrations for security risks before and after deployment
- Design and deploy guardrails, sandboxes, and controls to limit AI agent capabilities
- Build or implement AI identity and access management systems
- Detect and mitigate prompt injection attacks across Plaid's AI surface
- Conduct AI-focused security reviews and establish security standards
- Reduce AI-related incidents while accelerating safe AI shipping
What they're looking for
- AI security and threat modeling
- Guardrail and sandbox design/deployment
- AI identity and access management
- Prompt injection detection and mitigation
- Cloud security and infrastructure security
- Security review and SDLC practices
- Detection and response systems
- Offensive security or vulnerability discovery
Benefits
- Comprehensive medical, dental, and vision coverage
- 401(k) retirement plan
- Equity compensation
- Work on cutting-edge AI security challenges
- Cross-functional impact on product and business
- Seattle office location with established Plaid presence
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Plaid
Plaid builds infrastructure that connects users to their financial data and enables secure financial transactions through APIs and integrated experiences. The company is hiring Full Stack Software Engineers, Backend Engineers, Security Engineers, and Sales Engineers to develop and support its platform for millions of users.
- Website
- plaid.com
Likely interview questions
- Walk us through a time you discovered an AI security vulnerability in production—how did you identify it and what controls did you implement?
- Describe your hands-on experience with guardrails or sandboxes. What technologies did you use and what constraints were most challenging to enforce?