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Native Security is redefining cloud security by providing a unified abstraction layer across AWS, Azure, and GCP. Founded by AWS and Dome9 veterans and backed by $42M in funding, the company enables security teams to define and enforce policies without mastering complex cloud-specific configurations.
As an AI Expert in Engineering, you will own AI-powered product capabilities end-to-end—from concept through production. You'll collaborate with Product, Engineering, and Security domain experts to build practical, reliable AI systems that improve how teams operate and secure cloud environments. This is an applied role focused on shipping, adoption, and measurable outcomes.
Key responsibilities include:
- Building and shipping AI features that enhance cloud security operations, such as control recommendations, drift detection explanations, guided remediation, policy generation, investigations, and workflow automation.
- Designing and implementing AI agents tuned for cloud-security tasks, including frameworks for tools, permissions, orchestration, guardrails, and automation.
- Applying deep cloud security domain knowledge (IAM, network controls, logging/telemetry, posture management, governance, misconfiguration detection, threat scenarios) to guide product decisions and ensure safe, correct AI behavior.
- Integrating with multi-cloud APIs and security controls (AWS, Azure, GCP, OCI) to build end-to-end flows from user intent to actionable outcomes.
- Creating and maintaining evaluation methods for model quality (accuracy, coverage, hallucination rates, latency, cost, user satisfaction) and building tooling to continuously measure production performance.
- Establishing data and feedback loops to keep systems relevant (user corrections, outcome tracking, regression testing, prompt/model iteration).
- Staying current with practical LLM and agent advancements, adopting useful patterns into production.
- Engaging directly with users, testing MVPs in production, iterating based on feedback, and supporting customers as needed.
Required qualifications:
- 5+ years in cloud security, cloud engineering, or building security products for cloud environments.
- Deep knowledge of public cloud security fundamentals: IAM, network security, logging/monitoring, governance, and posture management.
- Strong production coding skills for end-to-end feature development (not just notebooks or POCs).
- Hands-on experience building modern AI systems (LLMs, retrieval, structured outputs, agents/tool use, evaluations) with a focus on reliability, security, and guardrails.
- Practical engineering instincts: ability to trade off quality, speed, and cost while shipping incrementally without breaking trust.
- Excellent cross-functional communication and collaboration skills.
- Fluency in English.
Ideal candidates think like product engineers, are focused on impact and users, love shipping, and are excited about building AI systems that operate safely in real cloud environments with real consequences and real customers.
