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Vasco is building a revenue context layer for AI agents. The product structures CRM, billing, calls, and support data into a semantic layer where every metric is sourced, every definition is locked, and every answer is deterministic. The company was founded by experienced entrepreneurs (Guillaume and Seb) who previously built and sold a startup together.
As a Software Engineer on the Product Backend team, you will build core systems that power Vasco's AI agents, giving them the context, metrics, and playbooks needed to operate effectively. You'll report to the CTO and collaborate closely with product designers, DevOps, Platform, Data, and Agent teams. The mission is to make GTM agents production-ready: reliable, fast, context-aware, and triggered at the right moment.
Key responsibilities include:
- Backend-focused, full-slice development: Design, build, and ship backend systems including challenging components like the context graph and metric engine (where formula accuracy is critical). Own complete feature slices including frontend and data visualization when needed, with human review proportional to risk.
- Shaping to adoption: Collaborate with designers, engineers, and GTM teammates using Shape Up methodology. Turn intent and design into proper context for Claude to implement, then instrument releases and track adoption with your team.
- Infrastructure, security, and data trust: Build services that handle customer revenue data safely. Own infrastructure and security for the systems you build, with guidance from DevOps, since the product's promise depends on data customers can trust.
- AI-native practices: Help build backend harnesses and blueprints—standards that Claude reads to build the way you want—so more of your time goes to thinking, technical design, and shaping as AI handles mechanical coding.
- Incident ownership: Own your incidents. If you break something in production, you fix it. Step up for your team the same way, communicating fast and transparently so problems are known before customers encounter them.
The role is not entry-level. Problems are hard, architecture is complex, and you'll own technical decisions and their implementation. High autonomy and accountability are expected at every level, with scope growing with experience. You'll be expected to bring clarity to an often-foggy path, stretch beyond your comfort zone, stay open-minded, and wear more hats than your title suggests.
Vasco is an early-stage startup working at the frontier of what agents can do in GTM. The company emphasizes AI-forward practices both in daily work and in what they build for customers. Real autonomy exists to experiment, pick up new skills, and take on projects outside your formal role. The team is small and high-caliber, with experienced founders still building alongside you.
Compensation is positioned within the top of the national market, with stock options for every team member, uncapped paid time off, generous parental leave, and 100% employer-paid health benefits for you and dependents.
REQUIREMENTS:
- At least four years building customer-facing systems where your technical decisions trace back to customer problems they solve.
- Backend depth: You've designed, built, and operated production backend services. You design complete systems with the big picture of a feature in mind and are comfortable working on data-heavy infrastructure products. Experience with GCP, BigQuery, Node.js, TypeScript, Terraform, or equivalent cloud stack is ideal.
- AI-forward with healthy skepticism: You've made AI coding tools central to how you ship. You own and can defend the output you ship. You build the checks that let you ship with evidence. You can demonstrate how you run your development lifecycle with AI.
- High-stakes judgment: You've made decisions where the cost of being wrong was real at your scale and can walk through how you decided.
- Communication clarity: You can turn technical trade-offs into clear business impact for any audience, from developers to cofounders.
- Collaboration: You pull people into your work, enjoy working as a team, and contribute to the culture around you.
STAND-OUT QUALIFICATIONS:
- Shipped production services in Rust or Go (seriously considered alongside TypeScript/Node for high-performance, type-safe workloads).
- Built data or context layers that AI agents consume in production, including context graph architectures.
- Data engineering or DevOps range: can drop into SQL, data modeling, pipelines, infrastructure, CI/CD, or observability work when needed.
- Experience with semantic layers or BI tools (they use Cube); enjoy formulas and getting numbers exactly right.
