VP of Engineering

Atlanta preferred · Remote considered Significant equity Reports to founder

The Role

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.

About the Founder

Matt Dollacker is a Computer Science graduate and engineer-leader who is regularly at the terminal and the whiteboard. His prior company, InductiveHealth, sold for over $100M. He was named Emory University's Entrepreneur of the Year in Healthcare (2025). He trained his first deep neural network in 2017, has 200 GPUs in his garage, and treats building companies the way he treats building large technology systems — as an engineering challenge with different constraints. He is creating and self-funding FxM because he sees a narrow and closing window to build something transformative for the millions of established businesses navigating the AI shift.

What You'll Do

• 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.

Your Background

• Engineering leadership at a firm known for delivery — a top-tier consultancy, a technology-led professional services firm, or a company where you were the person who made complex systems actually work in production
• Demonstrated excellence in at least one dimension that can be externally verified: a top-tier institution on your resume, a highly respected open-source project on your GitHub, an exceptional delivery credential with a reference who'll vouch for it, or leadership at a company recognized for engineering quality
• Strong academic credentials in computer science, engineering, or a related field — because it signals how you think, not what you studied
• A track record of building and operating systems that run 24/7 in commercial environments where reliability matters — you've been paged at 3am and you've built the systems that eventually stopped paging you
• You are likely already working with AI tools and LLMs in your day-to-day, and you have strong intuitions about where they add leverage and where they don't — even if you haven't deployed AI systems into production yet
• You've built teams — small, high-performing teams where every person carries real weight
• You have strong opinions about software architecture, testing, and operational excellence — and you can defend those opinions with evidence, not dogma
• You thrive in execution, not invention — you get energy from taking a well-conceived technical vision and making it real at a level of quality the architect couldn't achieve alone

Likely Not a Fit If

• 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

Compensation

Base salary is reasonable but not top-of-market. The real opportunity is equity: significant equity ownership, vested over time , in a company with proven technology, live deployments, and a founder who has built and sold a $100M+ company before. These are early, post-revenue roles and the equity reflects this. Within 12 months, you and the founder will both know whether this is a generational wealth outcome or a great learning experience working at the frontier of AI.

With your application, please provide the base compensation you'll require. This is a filter — we want engineers who understand the equity math and are making a deliberate bet.