Lead and build the engineering organization for an AI-driven industrial commerce platform. Own architecture, data pipelines, ML and computer vision integrations, mobile sensor-enabled identification, enterprise system integrations (ERP/DMS), cloud infrastructure, CI/CD, and observability. Hire and mentor distributed teams, deliver rapid MVPs, and establish a scalable technical vision for identification, predictive enrichment, and commerce execution.
UP.Labs is hiring a hands-on Head of Engineering for a stealth startup building an AI-powered identification and commerce platform for a large, fragmented industrial aftermarket. The industry is massive, essential, and almost entirely un-digitized.
The core problem: the journey from how industrial equipment is manufactured to how it gets serviced in the field is fundamentally broken. Parts go unidentified. Wrong parts get ordered and returned at staggering rates. And the experts who know how to navigate this complexity are retiring with no system in place to capture what they know.
We're building the platform that fixes this end-to-end - starting with AI-powered visual identification and expanding into the full commerce layer: cross-reference, fit confirmation, inventory, and embedded ordering. The goal isn't just an identification tool. It's the commerce OS for this industry, with identification as the entry point.
As the first engineering leader, you'll own architecture, data infrastructure, and team building. You'll remain hands-on early, build the technical foundation from scratch, and set long-term technical direction as we scale. You'll also have access to the UP.Partners ecosystem - product, engineering, design, analytics, legal, talent, finance, and VC - to help accelerate execution.
In this role, you will:
- Lead and grow the engineering team, balancing hands-on development with strategic leadership.
- Architect and own the core platform, including data pipelines, AI/ML integration layers, machine vision systems, and scalable cloud infrastructure.
- Lead ingestion and transformation of complex structured industrial data — bridging the gap between how equipment is built and how it can be identified and serviced in the field.
- Build and oversee a mobile-first product experience, leveraging camera, LiDAR, and on-device sensors for AI-powered identification in the field.
- Design and ship robust integrations with OEM ERP and DMS systems to ingest inventory, pricing, and product data at scale.
- Partner with Product and Data Science to translate messy real-world industrial workflows into intelligent, automated systems.
- Drive best practices around security, CI/CD, observability, testing, and documentation.
- Establish the technical vision for AI-driven identification, predictive data enrichment, and closed-loop fulfillment execution.
- Manage and mentor distributed contributors (including nearshore/offshore teams) to build a high-performance engineering culture.
- Deliver quick MVP iterations while laying the foundation for enterprise scale.
Who you are:
- A technical leader who thrives at the intersection of AI, data, and enterprise SaaS.
- Hands-on and detail-oriented, comfortable writing code, reviewing PRs, and guiding architecture.
- Fluent in agentic AI development - you've worked with AI coding tools and understand how to build and lead teams in this new world order.
- Skilled at integrating complex enterprise data systems (ERP, DMS, supply chain, pricing engines).
- Experienced building products that combine data infrastructure with applied machine learning and computer vision.
- A collaborative partner who can work closely with product, data science, and business stakeholders - and represent the technical vision credibly in front of customers and investors.
- Capable of thriving in ambiguous, high-growth startup environments and scaling teams from 0→1 and beyond.
You should have:
- 8+ years in software engineering, including 4+ years leading small, high-output teams in early-stage environments.
- Strong backend or full-stack background and ability to remain hands-on.
- Proven experience scaling B2B enterprise SaaS platforms with real-time data pipelines and system integrations - you think in SLAs, uptime, and enterprise integration patterns.
- Expertise in cloud architecture, CI/CD pipelines, observability, and distributed systems.
- Experience working with AI/ML platforms (e.g., predictive models, computer vision, optimization engines, LLM copilots).
- Experience overseeing or building mobile app products (iOS/Android); familiarity with on-device sensor capabilities (camera, LiDAR) is a strong plus.
- Familiarity with ERP/CRM/DMS integrations - SAP experience is a plus.
- Experience leading or managing distributed teams (nearshore/offshore).
- Background in aftermarket, supply chain, manufacturing, or industrial systems (nice-to-have).
Why Join Us:
- Build and lead the engineering organization for a high-growth AI startup from day one - full ownership, meaningful equity.
- Solve a genuinely hard problem in a market that has been ignored by technology - the opportunity to be first matters here.
- Work directly with a CEO with deep commercial experience in the space who wants a technical co-founder-level partner - not someone behind the glass.
- Collaborate with UP.Labs, strategic OEM partners, and investors to drive real impact in a $20–30B+ market.
- Earn meaningful equity in a company positioned to transform how OEMs and distributors generate and protect revenue.
- Remote-friendly. We care about output, not location.
About UP.Labs:
We build high-growth technology startups that enable faster, cleaner, and safer movement of people and goods. Our platform is unique in three ways:
- Risk: We reward our entire team and ecosystem of partners with meaningful equity.
- Technology: We build and launch scalable technology products that form the basis for each venture.
- Industry Focus: We stay focused on the underlying fabric of retail mobility.
We work with corporate investors over a multi-year period to launch a portfolio of ventures. Our team is dedicated to the first year of a new venture's life cycle: from ideation to MVP build - and beyond.
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