At Cirql One, we are building the foundational layer that connects enterprise data to AI: a layer that doesn’t just relay information, but genuinely understands it, making business meaning explicit, trusted, and actionable at scale.

The problem we’re solving is deceptively hard. SAP runs the core operations of more than 90% of Fortune 500 companies. The data is reliable. The meaning, however, is buried inside 30 years of configurations, customizations, and undocumented logic that no AI system can interpret on its own. We’ve spent decades inside these environments. We know the problem intimately, and we’ve built our company around a single conviction: before AI can truly scale inside the enterprise, business meaning must be made explicit.

Cirql One is that missing link: an enterprise semantic intelligence layer that gives AI models and agents a shared, business-accurate understanding of enterprise data. Our vision is to become the system of reason sitting above SAP, where intelligence, metrics, and AI automation are defined, governed, and executed.

For a talented, driven, and collaborative product engineer, working at Cirql One is an opportunity to shape how enterprise intelligence becomes something people actually want to use. You will own the full arc of a feature: from the data model to what lands on screen, on a team that takes both the craft and the mission seriously. That is our promise.
Tasks

• Own features end-to-end. Take a problem from idea to shipped product. You won’t hand things off at the frontend layer. You’ll see every feature through, and you’ll care about how it works and how it feels.
• Shape the product surface. Work directly with founders and design to define how the platform communicates with its users. Translate complex semantic intelligence into interfaces that feel clear, fast, and considered.
• Build the semantic core. Contribute to the platform that transforms raw enterprise data into structured, business-accurate meaning: from data extraction pipelines to semantic storage, versioning, and governance.
• Make principled technical decisions. Balance long-term platform coherence with the speed that early-stage demands. Push back when it matters. Adapt when it doesn’t.
• Amplify your impact with AI. Build AI capabilities into the product itself, and use the most powerful AI tools across your development workflow to move faster without compromising on quality.

Requirements

• Full-stack depth. Solid experience building and shipping user-facing products: comfortable across frontend interfaces, APIs, and backend systems. Technical decisions that hold up over time.
• Product sensibility. You care about UX, speed, and polish. You can spot a subpar solution and say so. You think about what to build, not just how.
• AI-first by default. Proficient with Claude Code and agentic workflows: not as a novelty, but as a core part of how you work every day.
• Sharp thinking. Break down complex problems methodically, reach well-reasoned conclusions. Comfortable making decisions under uncertainty.
• Clear communicator. Express ideas precisely in code, writing, and conversation. Walk non-technical colleagues through a technical decision without oversimplifying.
• Collaborative by default. Share knowledge freely, back the best idea regardless of where it came from. Someone colleagues genuinely want to build with.

Nice to have:

• Experience with data pipelines, ETL/ELT systems, or data lake architectures.
• Background in semantic systems: knowledge graphs, ontology mapping, or data catalogs.
• Exposure to SAP data structures or enterprise ERP environments.
• Prior startup experience. You know what early-stage actually means.

Benefits

• Competitive compensation. Fair and attractive pay that reflects the seniority and impact of the role.
• A growing team. We are scaling deliberately, bringing in people who raise the bar. Your scope grows as the company does.