Senior AI Security Lead

Senior Staff AI Security Lead at IonQ, Remote, US (remote). A senior-level full time position in the Corporate - Security team, listed under software engineering.

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%

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

• 8+ years in security engineering, platform engineering, or a closely related technical field, including significant hands-on software development.
• Demonstrated experience building and operating production systems with large language models — not prototypes or demos. You have shipped something real, dealt with its failure modes, and iterated on it.
• Strong software engineering fundamentals and fluency in Python (or an equivalent language), including API design, distributed systems, and cloud infrastructure (AWS, GCP, or Azure).
• Deep understanding of security engineering: identity and access management, secrets management, network boundaries, logging and detection, and secure software development practices.
• Working knowledge of AI-specific risk — prompt injection, jailbreaks, data leakage through model outputs, insecure agent tool use, model and dependency supply chain — and practical mitigations for each.
• A track record of driving technical change across an organization through influence rather than authority: building consensus, teaching, and shipping things people voluntarily adopt.
• Clear written and verbal communication. You can explain a system design to an engineer and the business case for it to an executive, in the same week.
• Sound judgment about where AI is genuinely useful and where it is not. We want an advocate who is also a skeptic.

• Experience with agent frameworks, orchestration, and the Model Context Protocol (MCP) or comparable tool-use standards.
• Experience building evaluation frameworks or LLM observability tooling.
• Background in security operations, detection engineering, or incident response — you have felt the toil firsthand.
• Experience with retrieval systems, embeddings, vector databases, and knowledge pipeline design.
• Familiarity with AI governance and assurance frameworks such as the NIST AI Risk Management Framework, ISO/IEC 42001, or the OWASP Top 10 for LLM Applications.
• Experience fine-tuning or evaluating open-weight models for domain-specific tasks.