Vice President of Engineering, VendorPM

• 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
• 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/TypeScript, GraphQL, 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

Desirable

• The Vice President of Engineering will partner with the founders and product leadership to build the engineering organization that takes VendorPM 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
• Own architectural decisions across our platform—you'll need to understand our NestJS/PostgreSQL/Apollo GraphQL/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 TypeScript 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
• Lead a team of ~10 engineers and set the bar for what great engineering looks like at VendorPM—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
• 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 maintain standards while capturing productivity gains
• Evaluate and integrate AI tooling into CI/CD, code review, and documentation workflows where it adds genuine value
• Own engineering delivery against product roadmap commitments with realistic estimation and clear communication when plans change
• Partner with Product on technical discovery to identify architectural prerequisites and risks before work enters sprints
• Build the muscle for shipping reliably—we want a team that hits its commitments and knows how to scope appropriately
• What You'll Work On:
• Near-Term (Q1-Q2 2026):
• Compliance system evolution: Our compliance domain is central to the product and needs architectural improvement to support building-specific requirements and clearer data models
• Role-based access control (RBAC): Solution is designed and ready for execution—needs engineering leadership to ship well
• Vendor data model improvements: Evolve our vendor data model to support the complexity of how enterprise PMCs categorize and manage vendors
• Engineering process foundations: Implement sprint planning, backlog management, and delivery practices that create predictable output
• Medium-Term:
• AI infrastructure: Extend our AI assistant capabilities, improve document parsing accuracy, and build the foundation for agentic procurement features
• Integration platform: Build robust patterns for ERP integrations (Yardi, MRI) that scale across customers
• Platform scalability: Address performance and architecture needs as we grow into larger enterprise deployments
• The Bigger Picture: You'll help build toward agentic procurement—a future where AI handles routine vendor management tasks end-to-end while property managers focus on relationships and exceptions. That vision requires thoughtful architectural decisions today, and an engineering organization capable of building sophisticated AI-powered features reliably

• We want to be a surprising and exciting place. But we also want you to know what to expect on your journey, starting with the interview process. Here’s what getting to know us will look like:
• Interview process step 1: Google Meets call with Recruiter
• Interview process step 2: Interview with the Hiring Manager
• Interview process step 3: Technical Interview with Product Consultant
• Interview process step 4: Take home assignment
• Interview process step 5: In office meet the team
• Artificial intelligence (AI) is not used at any stage of the hiring process for this role. All applications are reviewed, assessed, and selected by real people. We are committed to a fair and transparent hiring process