About This Role
As Biogen's AI Security Engineer, you are a hands-on technical specialist responsible for securing the organization's rapidly expanding use of artificial intelligence, machine learning, and generative AI technologies. This role operates at the intersection of cybersecurity engineering and AI/ML systems — ensuring that AI deployments across Biogen are resilient against adversarial threats, data poisoning, model manipulation, prompt injection, and unauthorized data exposure.
The AI Security Engineer will design, implement, and operate security controls purpose-built for AI workloads — spanning model development pipelines, inference endpoints, training data protection, and AI-assisted automation platforms. This role is responsible for translating emerging AI threat frameworks (MITRE ATLAS, OWASP AI Top 10, NIST AI RMF) into actionable engineering controls that protect Biogen's AI investments without impeding innovation velocity.
This position requires a rare combination of software engineering depth, AI/ML systems knowledge, and cybersecurity expertise. The scope demands:
Deep technical proficiency across AI/ML security, cloud security, and application security — with the ability to build production-grade security tooling, not just assess or advise
Autonomous execution — independently identifying AI security gaps, designing solutions, and implementing them end-to-end without requiring constant direction
Adversarial mindset — thinking like an attacker to identify how AI systems can be subverted, manipulated, or exploited before threat actors do
Cross-domain fluency — bridging the gap between data science teams building AI and security teams protecting the enterprise, translating between both worlds.
What You’ll Do
AI Threat Detection & Response Engineering
Design, build, and operate detection capabilities for AI-specific threats including prompt injection, model extraction, training data poisoning, and adversarial input attacks
Develop and maintain AI security monitoring pipelines that correlate signals across LLM interactions, API gateways, and model inference endpoints
Engineer automated response playbooks for AI security incidents — including model quarantine, token revocation, and session termination
Continuously research emerging AI attack vectors and translate findings into detection signatures and prevention controls
Operate and tune CrowdStrike AI Runtime Defense (AIRD) and Microsoft Purview AI governance controls across the environment
AI Platform Security Engineering
Implement security controls across AI development and deployment platforms (AWS SageMaker, Azure OpenAI, Databricks, internal ML pipelines)
Secure model training environments — including data access controls, compute isolation, artifact signing, and pipeline integrity verification
Engineer guardrails for generative AI usage — content filtering, DLP integration, output validation, and session-level access controls
Design and enforce AI model governance controls including model registry security, version control integrity, and deployment approval gates
Perform security architecture reviews of new AI services and integrations before production deployment
AI Risk Assessment & Framework Implementation
Conduct technical risk assessments of AI systems using MITRE ATLAS, OWASP AI Top 10, and NIST AI RMF frameworks
Perform adversarial testing (red teaming) of AI models and AI-integrated applications to identify vulnerabilities before production deployment
Develop and maintain AI security standards, reference architectures, and secure development guidelines for AI/ML teams
Evaluate third-party AI services and vendor AI integrations for security posture, data handling practices, and supply chain integrity
Security Automation & AI-Augmented Defense
Build and maintain AI-powered security tools that augment the cybersecurity team's detection and response capabilities
Develop integrations between security platforms (CrowdStrike, Microsoft Defender, Okta,…