Eliza - Senior Forward Deployed Engineer, Cyber
Requirements
• Deep practical cybersecurity expertise, including hands-on blue-team experience in detection, investigation, response, or defensive security engineering, with specific examples of decisions and outcomes you personally owned. • Strong software engineering skills beyond security scripting: production code, integrations, debugging, testing, and maintainable system design. • A record of taking complex technical work from problem definition through deployment, validation, and operational handover. • Practical AI fluency, including effective use of AI development tools and sound judgment about model evaluation, failure modes, and human review. • Experience deploying and troubleshooting software in cloud or customer-hosted environments, with practical understanding of identity and access, APIs, delivery pipelines, and production observability. • Clear customer-facing communication and the ability to translate an ambiguous need into technical requirements and acceptance evidence. • Senior technical judgment: work independently, make tradeoffs explicit, recognize the limits of your expertise, and escalate material risks early. • Ownership of useful outcomes, including verification, documentation, and adoption after the initial build. • Preferred • Additional depth in application security, AI agent security, security developer tooling, or purple-team and red-team work. • Consulting, professional services, forward-deployed engineering, or comparable direct customer delivery experience. • Experience with vulnerability remediation, investigation automation, identity and access controls, or secure AI integrations. • Experience in defense or other demanding security environments, or a record of creating reusable technical capabilities across engagements.
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 a tractable problem, baseline, architecture, and acceptance criteria; explain what to build, defer, or escalate. • Align engineering work with measurable security outcomes, such as faster investigation or validated remediation, and make scope, dependencies, and tradeoffs visible. • 2. Build and integrate AI-enabled security workflows • Write, review, and debug production software that connects models, APIs, telemetry, and security systems to support 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 the appropriate customer owners, including configuration, access, CI/CD, monitoring, and integration failures. • Establish clear release, rollback, and operating procedures for the capability you deliver. • Resolve issues across system boundaries and surface technical or delivery risks early with a recommendation. • 4. Evaluate quality and apply security judgment • Threat-model AI-enabled workflows and test realistic failure cases, including prompt injection, sensitive-data exposure, and unsafe tool actions, before treating a demonstration as production-ready. • Build practical controls across prompts, data flows, APIs, and cloud services, including least-privilege access, secrets protection, auditability, and human approval for consequential actions. • Verify that fixes address the original problem, document limitations, and involve specialist reviewers when needed. • 5. Earn customer trust through technical delivery • Explain architecture, evidence, risks, and tradeoffs clearly to technical and nontechnical stakeholders. • Challenge requests that would undermine security or usefulness and offer practical alternatives. • Work with delivery and commercial partners on commitments and additional opportunities grounded in demonstrated customer value. • 6. Leave maintainable capabilities behind • Produce clear documentation, interfaces, runbooks, and knowledge transfer so customer teams can operate and extend the work. • Agree operating ownership and handover criteria, or continued operation when the engagement calls for it. • Turn proven solutions and lessons into reusable engineering patterns, evaluation cases, and playbooks; share technical guidance with colleagues.
Benefits
• Competitive compensation (base salary + performance incentives tied to client outcomes). • Equity options in a growing AI services company. • Exposure to a wide range of industries and high-impact AI problems. • Travel opportunities for on-site client engagements (if desired). • A collaborative, mission-driven team passionate about the real-world impact of AI. • Fully remote work. • Ownership of substantive cyber engineering work and the opportunity to shape reusable approaches to AI-enabled security delivery.
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