Founding CTO / Head of Engineering

Founding CTO / Head of Engineering at Coronin AI Founding CTO / Head of Engineering About Us We're building a physical AI platform for skilled trades. Trade workers are the backbone of every industry, and they're disappearing. Most AI on the market was designed for office workers and adapted for everyone else. We're not doing that. We're building a dedicated system, from the ground up, to give the people who work with their hands the same caliber of AI tools that office workers have had for years. We’ve raised a $1M pre-seed round and secured pilot access to field sites. We’re looking for a Founding CTO / Head of Engineering to take this from concept to product. Who You'll Work With Solo founder with a commercial, M&A, fundraising, and operations background spanning asset-light mobility platforms, deep-tech, and skilled trades. The founder has scaled startups into publicly-traded international platforms, raised hundreds of millions in funding throughout their career, and has operated a skilled trades business for the past few years. You will be hire #1. The founder handles product, business, and fundraising. You will own everything technical. The Role The core technical challenge: build a multimodal AI system that works in uncontrolled physical environments, where lighting changes by the hour, camera angles are constrained by physical workspace, and the system must identify and distinguish between hundreds of similar-looking components and detect anomalies that are invisible to untrained eyes. Coordinate vision models, language models, and domain-specific knowledge retrieval into a single real-time pipeline. There’s no public dataset for this. Build production inference for multimodal AI where latency matters because a human is waiting in real time. Design the orchestration layer that routes between vision and language models, handles escalation, and recovers from uncertainty. Build RAG systems over domain knowledge that was never digitized for AI: technical docs, tribal expertise, manufacturer specs across thousands of product variants. Build the native mobile application, including on-device integrations and AI-powered features. Ship production cloud infrastructure: compute, auth, data persistence, and monitoring. Put it in the hands of real users at pilot sites and fix what breaks. Architect a platform that expands across trade verticals through configuration, not code rewrites. What the First 90 Days Look Like Days 1-30: Full product and technical context download. Assess the current state of the project: what to keep, what to rebuild, what to build from scratch. Deploy your first model to a staging environment. Days 31-60: Own the core AI pipeline. Ship production infrastructure for the vision and language model orchestration. Begin preparing for pilot deployment. Days 61-90: System running in a pilot environment with real users. Present the technical roadmap for the next 12 months. Begin sourcing the first ML/CV engineer hire. What We Need Must have: Computer vision in production. Deployed vision models to real systems with latency constraints. Experienced with model optimization, inference serving, and GPU resource management. Not just API calls. Experience with real-world visual conditions (e.g., variable lighting, cluttered backgrounds, objects at non-standard angles, etc.) is more valuable than experience with clean datasets. LLM and RAG proficiency. Built or significantly contributed to a production retrieval-augmented generation system. Can design, build, and optimize the full pipeline. The domain knowledge you’ll retrieve is fragmented, inconsistent, and was never structured for AI. This is not a “plug in a vector DB” problem. 0-to-1 track record. Built and shipped a product from zero. Made the foundational technical decisions when there was no codebase and no playbook. Startup experience. Has worked at an early-stage company (<20 people). Comfortable with ambiguity, speed, and making decisions with incomplete information. Clear communication. You'll operate as the technical counterpart to a business-focused founder. That means every architecture decision, risk, and trade-off needs to be communicated in plain language, not just made. Strongly preferred: Deep expertise in both computer vision and LLMs Model training and fine-tuning experience Based in NYC or willing to be in NYC regularly Prior technical leadership at an early-stage company Interest/experience/knowledge in skilled trades or blue-collar industries You Might Be a Fit If... You've left a job because the pace was too slow. You treat ambiguity as a starting point, not a blocker. You’ve been told something can’t be done, and you found a way to do it. You've shipped something you knew would need to be rewritten later, and you were right, and you'd do it again. You've made architecture decisions you had to live with for years, and you'd make some of them differently now. You have something to prove. You want to look back in five years and know that what you built changed how an entire industry works. This Role Is Not For You If… You prefer managing to building. This is an 80% hands-on-keyboard role for the first year. You need a fully-defined spec before you start work. You’ve never shipped a product with fewer than 20 people. You want product-market fit to be proven before you join. Why Join Full ownership. No legacy system, no existing team, no constraints. You own the entire technical vision, infrastructure, and execution. Every decision is yours. Real problem. The skilled trades are losing their most experienced workers to retirement, and nobody is replacing them fast enough. The work isn’t getting simpler. The tools haven’t kept up. Hard technical problem. You'll deploy production multimodal AI in uncontrolled physical environments and build domain-specific knowledge systems at scale. This is not another wrapper on GPT. Career-defining trajectory. In 18 months, you’ll be the technical leader of the engineering team building the AI platform an entire industry is waiting for. This is the role people point to on their resume and say, “I built that.” The Offer $175K-$225K base compensation, depending on experience 1-5% equity, depending on experience Full technical ownership from day one