Senior Staff AI Security Lead

About IonQ:  IonQ, Inc . [NYSE: IONQ] is the world’s leading quantum platform and merchant supplier - delivering integrated quantum solutions across computing, networking, sensing, and security. IonQ’s newest generation of quantum computers, the IonQ Tempo, is the latest in a line of cutting-edge systems that have been helping customers and partners including Amazon Web Services, and AstraZeneca achieve 20x performance results and accelerate innovation in drug discovery, materials science, financial modeling, logistics, cybersecurity, and defense. In 2025, the company achieved 99.99% two-qubit gate fidelity, setting a world record in quantum computing performance . Headquartered in College Park, Maryland, IonQ has operations in California, Colorado, Massachusetts, Tennessee, Washington, Italy, South Korea, Sweden, Switzerland, Canada, and the United Kingdom. Our quantum computing services are available through all major cloud providers, while we also meet the needs of networking and sensing customers across land, sea, air, and space. IonQ is making quantum platforms more accessible and impactful than ever before.   Location: This role can work onsite or hybrid from one of our offices. We are open to a fully remote option for the right candidate. Travel: Up to 10% Job ID: 1915 The Role:  We are hiring a senior technical leader to build the AI foundation our security organization runs on — and to make sure it actually gets used. This is a dual-mandate role. You will stand up the enterprise AI infrastructure that lets security teams safely build with large language models and agents, and you will lead the adoption effort that turns that infrastructure into measurable gains across detection and response, threat intelligence, vulnerability management, GRC, and security engineering. This is a hands-on senior individual contributor role. You will write code, design systems, and set technical direction. You will not manage a team, but you will lead through influence: partnering with engineering leaders, coaching analysts and engineers, and building the coalitions that make org-wide change stick. Expect roughly an even split between platform building and enablement work. You will report to the security leadership team and work closely with Platform Engineering, IT, Legal, Privacy, and the broader AI/ML organization. Responsibilities : Stand Up the Enterprise AI Infrastructure Build the platform. Design and build the shared platform security teams use to develop AI applications — model gateway and routing, authentication and authorization, secrets handling, rate limiting, cost attribution, and audit logging. Enable agentic systems. Establish patterns and reusable components for agentic workflows: tool and MCP server integration, sandboxed execution, human-in-the-loop approval gates, and least-privilege scoping for agent credentials. Wire in the data. Connect security's knowledge — runbooks, detection logic, past incidents, asset inventory, policy documents — to AI systems through well-governed retrieval pipelines with correct access controls and data classification enforcement. Make it measurable. Build the evaluation harness, regression suites, and observability that tell us whether an AI system is working: output quality, latency, cost, hallucination rate, and drift over time. Nothing ships to production without a baseline. Secure the stack itself. Implement guardrails against prompt injection, data exfiltration through model outputs, insecure tool use, and supply-chain risk in models and AI dependencies. Red-team the platform and the applications built on it. Set the standards. Own the reference architecture, golden paths, and internal documentation so that a security engineer can go from idea to reviewed prototype in days rather than months. Drive AI Adoption Across the Security Organization Find the real use cases. Work directly with detection engineering, IR, threat intel, vulnerability management, GRC, and security operations to identify where AI meaningfully reduces toil or improves quality — and where it does not. Kill weak ideas early. Build the flagship applications. Deliver a small number of high-visibility wins yourself. Nothing drives adoption like a working tool that saves an analyst two hours a day. Teach the organization. Run enablement programs — office hours, workshops, internal documentation, prompt and agent design patterns, brown-bags — that raise the AI fluency of the entire security organization, not just the engineers. Measure adoption honestly. Define and report the metrics that show whether adoption is real: active users, workflows automated, analyst hours returned, time-to-detect and time-to-respond improvements, quality deltas against human baselines. Establish governance that enables. Partner with Legal, Privacy, and Compliance to establish the acceptable-use policy, review process, and risk framework for AI in security workflows — des