VP of Engineering

You will lead the engineering team that builds, deploys, and operates a new kind of workforce — AI employees that work alongside humans in real businesses, every day, handling real commercial transactions. We call them Force Multiplier Agents (FMAs). They're in production. They're generating revenue and the team is rapidly scaling delivery. The platform needs an engineering leader who can take the founder's technical architecture and drive the next phase of implementation at scale.

To be direct: this is the founder's technical world. The architecture exists, the design decisions have been made with conviction, and the system is working. You are not being hired to reimagine the technical vision — you're being hired to lead its execution with excellence. You'll own the engineering team (human and AI), the build quality, the operational reliability, and the pace of delivery. You will manage AI agents as part of your engineering workforce. This is the job. You report directly to the founder.

This is a ground-floor technical leadership role. Significant equity ownership, vested over time. These are early, post-revenue roles and the equity reflects this. The founder is fully funding the company, with cashflow positive targeted in the next 90 days alongside a capital raise.

• Take ownership of the engineering implementation: the runtime that powers FMAs, the monitoring and observability layer, the data pipeline, and the deployment infrastructure — executing against an established technical architecture
• Build systems that allow non-trivial AI agents to operate reliably in production business environments — where mistakes cost real money and trust
• Build and lead a hybrid engineering team of humans and AI agents, establishing engineering standards that apply to both
• Design and implement the evaluation, testing, and calibration systems that ensure FMA quality at scale — this is the hardest and most important technical problem we have
• Make build-vs-buy decisions with strong opinions and real tradeoffs — we operate on a foundation of open-source and API-based AI infrastructure, and your job is to know what to own and what to leverage
• Ship. Constantly. With quality. In an environment where the underlying AI capabilities are changing on a monthly cycle.

• You need to own the technical vision to be motivated — the architecture here is set, and your job is to make it sing, not to rewrite it
• You need a large engineering organization to be effective — you'll be building the team, not inheriting one
• You're uncomfortable with the pace of change in AI infrastructure — if the foundation shifting every few months feels threatening rather than exciting, this isn't the right environment
• You see engineering leadership as primarily a management role — you will be deep in the architecture, the code, and the production systems, especially in the first year
• You require top-of-market base compensation — the base here is reasonable, the equity is where the bet pays off
• You're skeptical that AI agents can meaningfully contribute to an engineering workflow — they already do here, and you'll be responsible for making them better
• Your instinct is to build everything from scratch — we leverage existing infrastructure aggressively and build only where we must
• You view a technical founder who stays close to the code as a problem rather than an asset