Jobs at Native (Now Hiring) — 21 open

About Native

Native is defining a new category in Cloud Security by empowering organizations to build truly secure-by-design environments. We provide a unified abstraction layer across AWS, Azure, and GCP, allowing security teams to seamlessly define and actively enforce policies without needing to master complex, cloud-specific configurations. Founded by industry veterans from AWS and Dome9, backed by $42M in funding, and trusted by Fortune 100 enterprises, we are looking for exceptional talent to take extreme ownership and help us scale the future of multi-cloud security.

The Role

As an AI Expert at Native, you’ll take full ownership of AI-powered product capabilities - from concept to production. You’ll work closely 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: we care about shipping, adoption, and measurable outcomes. You’ll engage with users, test MVPs in production, iterate quickly based on feedback, and make sure what you build holds up in the real world. And yes you’ll also jump in as a Support Hero when needed, helping customers succeed and learning directly from how they work. 

Key Responsibilities

• Build and ship AI features that improve cloud security operations (e.g., control recommendations, drift detection explanations, guided remediation, policy generation, investigations, workflow automation). 
• Design and implement AI agents tuned for cloud-security tasks, including the surrounding framework (tools, permissions, orchestration, guardrails, and automation). 
• Apply deep understanding of cloud security domains (IAM, network controls, logging/telemetry, posture management, governance, misconfiguration, threat scenarios) to guide product decisions and ensure the AI behaves safely and correctly. 
• Integrate with multi-cloud APIs and security controls (AWS/Azure/GCP/OCI), building end-to-end flows from “user intent” → “actionable outcome.  Create and maintain evaluation methods for model quality (accuracy, coverage, hallucination rates, latency, cost, and user satisfaction) and build tooling to continuously measure performance in production. 
• Build data and feedback loops to keep systems relevant over time (user corrections, outcome tracking, regression testing, prompt/model iteration). 
• Stay current with practical advancements in LLMs/agents and engineering patterns, and adopt what’s useful (not hype) into production

Requirements

What We’re Looking For

• 5 years of experience in cloud security, cloud engineering, or building security products for cloud environments.
• Deep knowledge of public cloud security fundamentals, such as:

IAM (roles, policies, permissions boundaries, identity federation)

Network security (security groups, NACLs, firewall constructs, private connectivity)

Logging and monitoring (cloud audit logs, flows, detections, telemetry pipelines)

Governance and posture (misconfigurations, guardrails, policy-as-code concepts)

• Strong coding skills with the ability to develop end-to-end production features (not just notebooks/POCs).
• Hands-on experience building with modern AI systems (LLMs, retrieval, structured outputs, agents/tool use, evals) and a mindset for reliability, security, and guardrails.
• Practical engineering instincts: you know how to trade off quality/speed/cost and ship incrementally without breaking trust.
• Excellent communication and team collaboration skills; comfortable working cross-functionally.
• Fluency in English.

You’ll be a great fit if

• You think like a product engineer: focused on impact, users, and shipping.
• You love getting things done and taking ownership end-to-end.
• You’re excited about building AI systems that operate safely inside real cloud environments — with real consequences and real customers.

Why Join Now?

This isn’t just another AI job.

It’s a chance to build practical AI that makes multi-cloud security actually work — for the teams living it every day.

Let’s get to work.