Vice President of Engineering

The Role   The Vice President of Engineering will partner with the founders and product leadership to build the engineering organization that takes Vendor

PM to its next stage of growth. You'll report directly to the COO and own technical architecture, engineering culture, and delivery execution.
We've moved fast and hit our growth stride with signed enterprise customers, and built a platform that 10,000+ buildings and 70,000 vendors depend on daily. That speed created a strong foundation—and also the kind of technical and process debt you'd expect from a company that prioritized shipping over polish. Now we need a leader who can build the engineering maturity and discipline to support the next phase: larger customers, more complex requirements, and a product vision centered on agentic procurement.
This is a hands‑on leadership role. You'll update code standards, make architectural decisions you can defend technically, and work directly with engineers to solve hard problems. But you'll also shape the longer‑term vision—building toward AI systems that handle routine vendor management tasks end‑to‑end while making nuanced judgment calls about when to pay down debt, when to refactor, and when to ship.
You’ll join a company with strong product leadership, a clear strategic direction, and founders who are deeply involved in the business. What we need is an engineering leader who can match that clarity on the technical side—someone who can build a culture of ownership, quality, and velocity that scales with us.
We expect you to be hands‑on with AI tooling. If you're not already using Cursor, Claude, or similar tools to accelerate your own work and thinking about how AI changes engineering team productivity, this isn't the right fit.
What we are looking for     Deep experience (10‑15 years) building or significantly improving an engineering organization—you've shaped culture and process, not just inherited a working machine
Deep technical fluency with modern web architectures—you've built and scaled systems on AWS/GCP with Node.js/Type Script, Graph

QL, React, and PostgreSQL
Track record of improving engineering delivery predictability and quality while maintaining team morale and velocity
Hands‑on leadership style—you stay close to the code and can contribute technically when needed
Experience integrating AI/ML capabilities into production systems
Comfort with ambiguity and competing priorities: you've worked in startups or growth‑stage companies where judgment matters more than playbooks
Strong opinions on engineering culture and process, informed by experience with what actually works

What you’ll be doing   Technical Leadership     Own architectural decisions across our platform—you’ll need to understand our NestJS/Postgre

SQL/Apollo Graph

QL/React stack deeply enough to guide its evolution and make build‑vs‑buy decisions
Drive the technical strategy for AI integration, including evaluating when to extend our Type Script stack versus introducing Python or other languages better suited for AI/ML and data‑intensive workloads
Make nuanced tradeoffs between paying down technical debt and shipping features—and build team judgment for making these calls well
Building Engineering Culture     Lead a team of ~10 engineers and set the bar for what great engineering looks like at Vendor

PM—we believe strong engineering culture is built through in‑person collaboration, whiteboarding sessions, and solving hard problems together
Shift team structure and processes toward true full‑stack ownership, moving away from rigid frontend/backend distinctions that create handoff friction and limit engineer growth
Build processes that create accountability and visibility without bureaucracy—sprint planning, estimation, and delivery tracking that enable engineers to own outcomes, not just tasks
AI‑Forward Development & QA Practice     Establish practices for AI‑assisted development across the engineering team—we expect engineers to leverage tools like Cursor, Claude, and Copilot to accelerate their work
Build QA and testing approaches that account for AI‑generated code: review practices, test coverage expectations, and quality gates that…