Staff Security Engineer (A.I)

Compass; Boston (MA); On-site; USD 180,000 - 240,000; Full time

Job summary

Compass is embedding generative and agentic AI into the products our agents and clients use every day. This role owns the security of that surface and serves as the technical authority on how Compass builds AI features safely, from the model and retrieval layer to tool invocation and client display.

You will work with the AI platform and product engineering teams to define security standards, perform threat modeling for new AI capabilities, and build production guardrails and monitoring to

Qualifications

• 7+ years in security engineering, with AI/ML or LLM security focus.
• Strong understanding of transformer architectures, inference pipelines, and LLM risks.
• Experience threat modeling complex distributed systems and remediation with engineering teams.
• Proven ability to translate AI risk to business impact for leadership.
• Familiarity with MITRE ATLAS, NIST AI RMF or comparable frameworks.

Responsibilities

• Own AI product security roadmap and multi-year security strategy.
• Threat model AI capability builds and perform architectural reviews.
• Build production guardrails for LLM inputs/outputs and data egress checks.
• Constrain autonomy with blast-radius controls and human-in-the-loop checks.
• Secure retrieval layer and enforce data access controls across RAG/setup.
• Harden AI supply chain with third-party models and providers.
• Instrument detections and telemetry for AI abuse in production.
• Ship reusable secure AI patterns and CI/CD checks for engineering teams.
• Advise executives on AI architecture risk with concrete roadmaps.

Skills

Python

Go

Security engineering

Threat modeling

AI/ML security

Adversarial ML

Red teaming

AWS

GCP

Kubernetes

IAM

DevSecOps

MITRE ATLAS

NIST AI RMF

RAG

Tools

AWS

GCP

Kubernetes

IAM

API security

CI/CD

Job description

• 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

You communicate well across audiences, translating emerging AI risk into technical roadmaps for engineers and business impact for leadershipYou are proactive about finding and closing gaps in AI/ML workflows through tooling rather than policyAutomation is your default approach to security, especially when securing complex AI systems at scaleYou have a deep working understanding of transformer architectures, inference pipelines, RAG, prompt engineering, and the OWASP Top 10 for LLMsYou write production code, strong proficiency in Python or Go, with experience building real integrations and security pipelines, not just prototypesDemonstrated experience securing a production AI or ML system end to end, development lifecycle, deployment, and runtimeStrong strategic communication skills with a proven ability to translate complex technical AI risk into business impact for executive leadershipExperience threat modeling complex distributed systems and driving remediation with engineering teamsStrong application and cloud security fundamentals: AWS and/or GCP, Kubernetes, IAM, API security, and DevSecOps practices7+ years in security engineering, with 2+ years focused on AI/ML or LLM application securityExpertise in adversarial ML and LLM risks including prompt injection, model poisoning, data extraction, and unsafe tool use, with hands-on red teaming experienceExperience with multi-agent autonomous systems or Model Context Protocol (MCP) based architecturesFamiliarity with MITRE ATLAS, NIST AI RMF, or comparable AI threat/risk frameworksBackground building evaluation and safety-testing harnesses for LLM applicationsExperience in a regulated or consumer-data-heavy environment where model outputs touch customer-facing decisionsPrior work on AI platform security architecture or secure-by-default AI SDKs