Senior Staff AI Security Lead

IonQ, Inc.; United States; Vor Ort; USD 180.000 - 280.000; Vollzeit

Zusammenfassung

IonQ, Inc. is hiring a senior technical leader to build the AI foundation for our security organization and ensure it is actually used.

This dual-mandate role is hands-on and non-managing, requiring you to code, design systems, and set the technical direction. You will stand up the enterprise AI infrastructure for safe security teams to build with large language models and agents, while leading adoption to deliver measurable gains across detection, response, threat intel, vulnerability

Qualifikationen

• Demonstrated experience building and operating production systems with large language models and associated tooling.
• Strong software engineering fundamentals with fluency in Python, API design, distributed systems, and cloud infrastructure.
• Deep understanding of security engineering: IAM, secrets management, network boundaries, logging, and detection.
• Experience with AI risk: prompt injection, data leakage, insecure tool use, supply chain risks.
• Track record of influence-led technical change across large organizations.

Aufgaben

• Stand up enterprise AI infrastructure enabling secure LLM and agent work for security teams.
• Lead adoption efforts to drive measurable gains across detection, response, threat intel, vulnerability mgmt, GRC, and security engineering.
• Write code, design systems, set technical direction, and build reusable platform components.
• Build flagship applications and guardrails; ensure eval, observability, and baseline metrics for production AI.

Kenntnisse

Python

Distributed systems

Cloud infrastructure

Security engineering

AI governance

Jobbeschreibung

• 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
• 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
• 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 — designed to unblock teams rather than stall them
• Stay ahead of the field. Maintain the organization’s point of view on where AI capability is heading and what it means for our security roadmap, staffing, and threat model. Evaluate vendors and open models on the merits
• —-What Success Looks Like—-
• —-First 90 Days
• You have mapped the current state — what security teams are already doing with AI, sanctioned or not — and published a prioritized assessment of gaps, risks, and opportunities
• A minimum viable AI platform is running: gateway, authentication, logging, and cost attribution, with at least one security team building on it
• An acceptable-use policy and lightweight review process for AI in security workflows is drafted and socialized
• —-First 6 Months
• Two or more production AI applications are in daily use by security teams, with measured impact
• The evaluation and observability layer is in place; no AI system reaches production without a quality baseline and monitoring
• Enablement is operating on a regular cadence, and engineers outside your immediate orbit are shipping their own AI-assisted workflows on the golden paths you built
• —-First Year
• AI is a normal part of how the security organization works, with adoption and impact reported through metrics leadership trusts
• The platform supports agentic workflows safely, with guardrails that have been tested adversarially
• The role has clearly outgrown one person, and you have made the case for what the team around it should look like

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 itWorking 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 eachStrong 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 practices8+ years in security engineering, platform engineering, or a closely related technical field, including significant hands-on software developmentSound judgment about where AI is genuinely useful and where it is not. We want an advocate who is also a skepticClear 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 weekA track record of driving technical change across an organization through influence rather than authority: building consensus, teaching, and shipping things people voluntarily adoptExperience with retrieval systems, embeddings, vector databases, and knowledge pipeline designPrior experience as a first or founding hire on a platform or capability that later scaled organization-wideExperience fine-tuning or evaluating open-weight models for domain-specific tasksFamiliarity with AI governance and assurance frameworks such as the NIST AI Risk Management Framework, ISO/IEC 42001, or the OWASP Top 10 for LLM ApplicationsExperience building evaluation frameworks or LLM observability toolingBackground in security operations, detection engineering, or incident response — you have felt the toil firsthandExperience with agent frameworks, orchestration, and the Model Context Protocol (MCP) or comparable tool-use standards