Full Stack Product Engineer (AI-Native)

Full Stack Product Engineer (AI\-Native) **Location**: Austin, TX preferred (hybrid); open to remote for the right candidate **Type**: Full time **Reports to**: VP of Engineering **Compensation range**: $105,000 to $130,000 base salary, plus benefits and any applicable equity or bonus compensation About Living Security Living Security is a B2B SaaS company in human risk management — we help large enterprises understand and reduce the human side of security risk. Our customers are major enterprise security teams, and our platform increasingly runs on AI: AI\-generated training content, risk scoring, and AI\-native product capabilities are core to where we're headed, not bolt\-ons. We're a small engineering team with an unusually high output\-per\-engineer model, and we're hiring builders who want that leverage. About the Role We are hiring a product engineer who ships outcomes, not tickets. This is a full stack, high autonomy role for someone who develops almost entirely through AI tools — Claude Code or similar — but who can actually drive the AI because they genuinely understand how web applications work. You know the full architecture of a modern webapp: frontend, backend, APIs, databases, auth, background jobs, deployment. You know the common failure modes, the framework tradeoffs, and the problems that show up in real production systems. That understanding is what lets you direct AI aggressively instead of just accepting what it produces. You don't need a decade of experience. You need to be a true generalist who has built complete webapps end to end, understands why they're structured the way they are, and can take an ambiguous business problem and turn it into working, shipped software without someone writing you a spec first. The work in front of us is concrete: LLM\-powered product features, integrations into the Microsoft enterprise ecosystem (Entra ID, Outlook add\-ins, Teams apps), real\-time reporting and dashboards, and the enterprise platform capabilities — identity, permissions, data pipelines — that large security organizations depend on. How We Work AI tools do most of the typing here. Your value is in the judgment layer: knowing what to build, how to structure it, when the AI's output is wrong, and what "done" actually means for the customer. If you've used AI coding tools as your primary workflow — not as autocomplete, but as the way you plan, build, test, and debug — this will feel natural. If you've mostly worked from detailed tickets inside one slice of a large system, it won't. We run a continuous\-flow model, not sprints: work is scoped into roughly one\-week shippable deliverables, milestones carry the multi\-week arcs, and there's minimal ceremony between you and production. Success here is measured in shipped customer outcomes, not story points or activity. One thing to be clear\-eyed about: this is a startup with hard enterprise commitments and real deadlines, and the pace reflects that. We move with genuin