Senior Forward Deployed Engineer, Cyber
Eliza · United States
About this role
**Senior Forward Deployed Engineer, Cyber** **About Eliza** Eliza is a technology services company and an Advanced-tier OpenAI partner dedicated to helping organizations build and deploy cutting-edge AI solutions. We deliver real-world AI applications—from generative AI and custom LLM integrations to predictive analytics and intelligent automation—through deep technical expertise and hands-on client collaboration. **Job Summary** As a **Senior Forward Deployed Engineer (Cyber)**, you will build and deploy **AI-enabled security solutions** with customers. You’ll bring cybersecurity expertise and strong software engineering skills across discovery, architecture, integration, deployment, evaluation, and handover—turning ambiguous security problems into useful, maintainable capabilities in the customer’s environment. This is a **fully remote** role with **substantial autonomy** and **direct client contact**. You’ll work closely with security practitioners, engineering teams, and delivery/commercial partners to make tradeoffs clear and keep commitments credible. Eliza is **AI-native**: you’ll use agentic coding tools (including OpenAI Daybreak), inspect and verify outputs, and make practical decisions about where models help, where they fail, and where human judgment belongs. --- ## Key Responsibilities 1. **Turn Cyber Needs into a Technical Plan** - Lead technical discovery with customer security and engineering teams to understand workflows, systems, data, risks, and operating constraints. - Define tractable problems, baselines, architectures, and acceptance criteria—clarifying what to build, defer, or escalate. - Align engineering work with measurable security outcomes (e.g., faster investigation or validated remediation) and make scope/dependencies/tradeoffs visible. 2. **Build and Integrate AI-Enabled Security Workflows** - Write, review, and debug production software connecting models, APIs, telemetry, and security systems for detection, investigation, and vulnerability remediation. - Choose tools and architecture that fit the engagement (balancing speed, reliability, maintainability, and cost). - Use AI coding tools effectively while retaining ownership of code quality, correctness, and security. 3. **Deploy and Operate in Customer Environments** - Own deployment and troubleshooting with customer owners (configuration, access, CI/CD, monitoring, integration failures). - Establish release/rollback and operating procedures for the delivered capability. - Resolve issues across system boundaries and surface technical/delivery risks early with recommendations. 4. **Evaluate Quality and Apply Security Judgment** - Threat-model AI-enabled workflows and test realistic failure cases (prompt injection, sensitive-data exposure, unsafe tool actions) before treating demos as production-ready. - Implement practical controls across prompts, data flows, APIs, and cloud services (least-privilege access, secrets protection, auditability, and human approval for consequential actions). - Verify fixes address the original problem, document limitations, and involve specialist reviewers when needed. 5. **Earn Customer Trust Through Technical Delivery** - Communicate architecture, evidence, risks, and tradeoffs clearly to technical and nontechnical stakeholders. - Challenge requests that undermine security or usefulness and propose practical alternatives. - Partner with delivery/commercial teams on commi
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