Principal Security Engineer
New
M
MQ Referrals OnlyFintech Security
This role can be performed remotely anywhere within the United States or from our Oakland office.Full-TimePrincipal
Salary218,300 - 321,000 USD per year
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Job Details
- Experience
- 10+ years of security engineering experience
- Required Skills
- AWSPython
Requirements
- 10+ years of security engineering experience with demonstrated technical leadership across multiple security domains
- Deep product security expertise: threat modeling, security architecture review, secure code review, API security, authentication/authorization design, and secure SDLC practices
- Experience with AI/ML security (adversarial attacks, model poisoning, prompt injection, data privacy, AI supply chain threats)
- Broad security fluency across infrastructure and enterprise security (endpoint, network, identity, cloud security)
- Experience working in cloud-native environments (AWS preferred)
- Strong programming skills in at least one language (Python, Java, Go, or similar)
- Knowledge of compliance and control frameworks relevant to financial services (PCI DSS, SOX, SOC2, NIST CSF)
Responsibilities
- Lead product security engineering for our payment platform—owning threat modeling, security architecture review, secure SDLC practices, and API security across the engineering organization
- Help mature our AI security program developing genAI controls, securing ML pipelines, and working alongside the Model Risk Office for model evaluations.
- Provide security architecture oversight across infrastructure and enterprise security—endpoint, network, VPN, and corporate security controls—ensuring technical standards are coherent across all security domains
- Shape how security engineering scales across the organization through tooling, frameworks, security champions engagement, and engineering partnerships
- Conduct security architecture reviews and threat modeling for new product features, APIs, and service integrations
- Perform security assessments of AI/ML model architectures, training pipelines, inference endpoints, and deployment infrastructure
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