About the Role
As Marqeta’s Principal Security Engineer, you will serve as the technical lead across our security engineering function. This role combines three critical responsibilities: leading product security engineering across our payment platform, building our AI security program as we scale generative AI and ML capabilities, and providing security architecture oversight across enterprise and infrastructure security.
Your primary focus will be product security and AI—threat modeling payment features, securing APIs, building genAI controls, and ensuring AI-powered capabilities ship securely. You'll also own the security architecture function and provide technical oversight for infrastructure security—endpoint protection, network security, VPN, and enterprise security controls—ensuring coherent security standards across everything we build and operate.
You'll partner closely with Product Security, Infrastructure Security, and Security Operations teams and serve as the security voice in our Model Risk Office. This is an individual contributor role with mentoring responsibilities and broad technical influence across the security, engineering, and business technology organizations.
We work Flexible First. This role can be performed remotely anywhere within the United States or from our Oakland office. We’d love for you to join us!
Key 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
The Impact You'll Have:
Product Security:
- Conduct security architecture reviews and threat modeling for new product features, APIs, and service integrations across the payment platform
- Define and maintain secure development lifecycle practices including secure code review standards, API security patterns, and authentication/authorization frameworks
- Develop self-service security tooling and developer-facing guardrails that reduce friction while maintaining security posture
AI Security:
- Lead security strategy and risk assessment for AI/ML systems including customer-facing AI products, fraud detection models, LLM integrations, and recommendation systems
- Build genAI security controls—prompt injection prevention, output filtering, model validation, and monitoring frameworks
- Perform security assessments of AI/ML model architectures, training pipelines, inference endpoints, and deployment infrastructure
- Evaluate and operationalize AI-powered security tools (e.g., AI-assisted code review, anomaly detection, automated threat intelligence) to improve security operations
Enterprise & Infrastructure Security Oversight:
- Provide technical oversight for infrastructure security including endpoint protection, network security, VPN, and enterprise security controls
- Ensure coherent security architecture standards across product, cloud infrastructure, and corporate environments
- Drive technical decisions for security tooling and controls that span the full environment—from developer laptops to production infrastructure
Across All Domains:
- Partner across Product Security, Infrastructure Security, and Security Operations teams as well as engineering, data science, and compliance
- Mentor security engineers and cross-functional teams, raising the organization's overall security engineering maturity
- Communicate security risks and strategy to executive and board-level audiences
Who You Are:
- 10+ years of security engineering experience with demonstrated technical leadership across multiple security domains; or equivalent combination of education and experience
- Deep product security expertise: threat modeling, security architecture review, secure code review, API security, authentication/authorization design, and secure SDLC practices
- Experience with or strong interest in AI/ML security—understanding of risks including adversarial attacks, model poisoning, prompt injection, data privacy, and AI supply chain threats. We want someone who is genuinely excited about AI technology and wants to secure it, not just govern it
- Broad security fluency across infrastructure and enterprise security—endpoint protection, network security, identity, and cloud security—even if your deepest expertise is in application and product security
- Experience working in cloud-native environments (AWS preferred) with familiarity across AI/ML services (Bedrock, SageMaker, etc.)
- Proven ability to build security frameworks, tools, and programs from the ground up
- Strong programming skills in at least one language (Python, Java, Go, or similar) with the ability to read and review code across multiple languages
- Experience with security assessment methodologies and risk management frameworks
- Working knowledge of compliance and control frameworks relevant to financial services (PCI DSS, SOX, SOC2, NIST CSF)
- Ability to communicate complex security risks to both technical and executive audiences
Nice to Have:
- Financial services or fintech experience strongly preferred
- Experience securing payment processing systems, card issuing platforms, fraud detection models, or transaction monitoring infrastructure
- Hands-on experience with LLM security: prompt injection mitigation, output filtering, RAG security, agent security patterns
- Experience with enterprise security platforms (EDR, SIEM, identity providers, network security tools)
- Experience with ML frameworks (PyTorch, TensorFlow) or background in data science / machine learning engineering
- Knowledge of AI governance, model risk management practices, and emerging AI regulatory frameworks (EU AI Act, NIST AI RMF)
- Background in supply chain security, CI/CD pipeline security, or secure software composition analysis
- Experience with privacy-preserving ML techniques (differential privacy, federated learning, secure multi-party computation)
- Experience with Kubernetes, containerized workloads, and Infrastructure as Code (Terraform)
- CISSP, CCSP, CISA, or other relevant security certifications
- Experience building and scaling security programs in high-growth environments