Principal Engineer

You will drive the organization's transformation into an AI-native engineering org. This is not a side project. The companies that win the next decade will be the ones whose engineering orgs absorb AI into how they build, not just what they ship. You will build the internal AI platforms, dev tooling, and engineering practices that make product development 10x more AI-leveraged than it is today. You own that mandate end-to-end: platforms, workflows, culture, and proof points. You will report to the founders and work closely with the CPO, with influence over every team in the org. This is a leadership role.

Responsibilities:

• Set technical direction for the product portfolio. Make the architecture calls others will live with for years.
• Set technical standards across services, platforms, and engineering teams. Raise the bar on system design, code quality, AI integration, and engineering velocity.
• Conceptualize and build internal AI platforms, evaluate management, agentic dev tooling, codegen pipelines, AI-assisted QA, retrieval, and inference infrastructure. Change the unit economics of building software.
• Operate at the bleeding edge of AI-native product engineering. Stay ahead of what's possible and bring it back in code and in how the team builds.
• Prevent the predictable failure modes of a scaling product org: unmaintainable systems, reliability decay, manual-QA bloat, and slow integration of AI primitives into the product.
• Stay hands-on. The people best positioned to challenge how we build are the ones still building.

Requirements:

• 12-14 years of engineering experience, anchored in a strong IC foundation. Most of your time today goes into architecture, technical direction, and platform building, but you still code when it sharpens a decision or proves a hypothesis.
• Cloud and platform expertise. 10+ years building scalable cloud/SaaS products, enterprise infrastructure software, or developer platforms.
• Technical depth in AI. Deep, current understanding of the AI landscape. Hands-on fluency with AI frameworks, foundation models, embeddings, and vector databases. You have shipped AI features, built AI platforms, and have opinions on what works and what doesn't in the real world.
• Proven ability to influence engineering leaders, product teams, and stakeholders to achieve outcomes in complex environments.
• Acts as a force multiplier leveraging technical credibility to drive alignment and overcome organizational barriers and friction points.
• Demonstrated ownership of large-scale architecture decisions in production (SaaS environment preferred).
• Fluency in both halves of the modern stack: classical distributed systems and the new stack of LLMs, evals, agentic systems, retrieval, and inference economics.
• A bias for building over reviewing. You write code, ship tools, and prove ideas with working systems.
• Track record of moving an engineering team's technical culture, not just its output.
• Ability to communicate complex technical ideas in simple terms to the executive team.