Sr. Staff AI Security Engineer, AI Native Platform

Life360 is a mission-driven company focused on keeping families connected through innovative technology. They are seeking a Sr. Staff AI Security Engineer to secure their AI infrastructure as it evolves, driving delivery across key security domains and contributing to architectural design decisions. This role involves building secure patterns, implementing access controls, and ensuring the safety of AI systems handling sensitive real-time data.

Responsibilities:

• Secure how Life360 accesses frontier models. Design, build, and iterate the access controls, policy enforcement, and authorization patterns that govern how systems interact with the frontier models they rely on
• Build secure patterns for MCP access and tool use authorization. Build and own the controls that vets, risk-tier, and govern how we integrate with external tools and services via MCP as adoption expands across engineering teams
• Design and build the identity and authorization model for autonomous agents: service identities, scoped credentials, and least-privilege access patterns. Define and enforce the trust boundaries that govern how agents interact across orchestration chains
• Design and build agentic observability and adversarial defenses. Build the telemetry pipelines and behavioral monitoring that provide visibility into AI system behavior. Implement architecture-level defenses against prompt injection and related adversarial attack classes
• Shape security for the common AI end-user platform. Lead design reviews, build access controls, data boundary enforcement, and abuse detection that keep a shared AI environment safe across users with different privilege levels
• Secure the shared knowledge layer. Define access control and data governance for retrieval augmented and reasoning systems, ensuring AI-powered tools don't surface sensitive data to the wrong systems or users
• Build AI supply chain integrity into the platform. Develop model provenance practices, service vetting, and dependency controls that keep the AI stack trustworthy as it grows
• Partner with Privacy, Legal, and Data Platform to ensure the right controls are built into pipelines handling real-time location, family relationship data, and data involving minors

Requirements:

• 12+ years in security engineering with depth in application security, cloud security, IAM, or detection — and a track record of building controls that earn adoption, not just approval
• Hands on builder shipping security controls that hold up in production. You're not an advisor but a practitioner that can define patterns that last
• Hands-on fluency with LLM and agentic systems. You've built with these tools, broken them, and shipped fixes for prompt pipelines, RAG architectures, and multi-agent orchestration from the inside
• Solid grounding in IAM for non-human systems: service identities, OAuth, secrets management, RBAC/ABAC, and least-privilege architecture at scale
• Experience with production telemetry and detection — defining detections and building response paths for threat surfaces without established playbooks
• Comfort with ambiguity and in-flight builds. You're energized by figuring things out — writing first-draft standards, testing approaches, and scaling what works
• Strong cross-functional communication and the ability to push back when it matters. You carry risk, tradeoffs, and technical decisions across engineering, product, and security leadership without losing precision — and can reshape a risky decision clearly and constructively
• Familiarity with NIST AI RMF, OWASP LLM Top 10, and adjacent compliance environments for consumer data at scale
• Bachelor's degree or equivalent experience in Computer Science, Information Security, or a related field
• Experience with frontier model API security, tool-use authorization patterns, or access governance for AI systems at scale
• Hands-on experience with multi-agent orchestration frameworks (LangGraph, AutoGen, CrewAI, or similar) and their trust, identity, and authorization challenges
• Familiarity with knowledge graph architectures, vector stores, or RAG systems — and the access control and data boundary problems they introduce
• Red teaming or adversarial testing against AI systems: prompt injection, jailbreaks, data extraction, model inversion, or supply chain attacks
• Background in consumer technology or another domain where personal data sensitivity is a core product obligation — not just a legal requirement
• Experience designing or reviewing security for internal enterprise AI platforms serving non-technical users