Staff Security Engineer, Compass

• Automation is your default approach to security, especially when securing complex AI systems at scale
• You have a deep working understanding of transformer architectures, inference pipelines, RAG, prompt engineering, and the OWASP Top 10 for LLMs
• You write production code, strong proficiency in Python or Go, with experience building real integrations and security pipelines, not just prototypes
• You are proactive about finding and closing gaps in AI/ML workflows through tooling rather than policy
• You communicate well across audiences, translating emerging AI risk into technical roadmaps for engineers and business impact for leadership
• 7+ years in security engineering, with 2+ years focused on AI/ML or LLM application security
• Demonstrated experience securing a production AI or ML system end to end, development lifecycle, deployment, and runtime
• Expertise in adversarial ML and LLM risks including prompt injection, model poisoning, data extraction, and unsafe tool use, with hands-on red teaming experience
• Strong application and cloud security fundamentals: AWS and/or GCP, Kubernetes, IAM, API security, and DevSecOps practices
• Experience threat modeling complex distributed systems and driving remediation with engineering teams
• Strong strategic communication skills with a proven ability to translate complex technical AI risk into business impact for executive leadership

Desirable

• Compass is embedding generative and agentic AI directly into the products our agents and clients use every day. This role owns the security of that surface
• You will be the technical authority on how Compass builds AI features safely, from the model and retrieval layer, through agent tool invocation, to what ultimately reaches a client's screen. You will work shoulder-to-shoulder with the teams building the AI platform so that platform standards and security standards are one bar, not two
• Own the AI product security roadmap. Define the multi-year vision, strategy, and standards for securing AI capabilities in the Compass platform, developed jointly with the AI platform and product engineering teams
• Threat model AI capability builds. Drive architectural reviews and threat modeling for new AI features, RAG pipelines, multi-agent workflows, and automated decisioning before they ship
• Build production guardrails. Architect, deploy, and automate real-time protections for LLM inputs and outputs, including injection mitigation, jailbreak defense, output verification, and sensitive-data egress checks on generated responses
• Constrain autonomy. Design blast-radius controls for autonomous agents: scoped permissions, human-in-the-loop checkpoints for consequential actions, action audit trails, and safe tool-invocation patterns
• Secure the retrieval layer. Enforce data access controls, source integrity, tenancy boundaries, and retrieval-boundary protections across RAG and vector store implementations
• Harden the AI supply chain. Assess the security posture, privacy controls, data retention, and training-use terms of third-party foundation models, inference providers, and vector stores used in production
• Instrument and detect. Build detections and telemetry for AI-specific abuse in production, anomalous tool invocation, prompt abuse patterns, unusual retrieval behavior, and data exfiltration through model responses and integrate them into our monitoring estate
• Automate the standard. Ship reusable libraries, CI/CD checks, and evaluation harnesses so secure AI patterns are the default path for product engineers rather than a review gate
• Advise and evangelize. Act as the primary technical consultant for AI architecture decisions and translate emerging AI risk into concrete engineering roadmaps for both engineers and executives