VP, Global Head of Engineering

Interface AI•San Francisco, CA

2d•Onsite

About The Position

Banking is being reimagined—and customers expect every interaction to be easy, personal, and instant. We are building a universal banking assistant that millions of U.S. consumers can use to transact across all financial institutions and, over time, autonomously drive their financial goals. Powered by our proprietary BankGPT platform, this assistant is positioned to displace age-old legacy systems within financial institutions and own the end-to-end CX stack, unlocking a $200B opportunity and potentially replacing multiple publicly traded companies. Ultimately, our mission is to drive financial well-being for millions of consumers. With over two-thirds of Americans living paycheck to paycheck, 50% holding less than $500 in savings, and only 17% financially literate, we aim to put financial well-being on autopilot to help solve this problem. Interface.ai is building the AI infrastructure for financial services — bringing agentic AI and conversational AI to the credit unions and community banks that serve everyday Americans. We're not a lab, we're not a demo company, and we're not burning runway on hypotheticals. We are in production, generating real revenue, and on a mission that actually matters: democratizing financial wellness for the millions of people who've never had a private banker. Backed by $30M in Series A funding and already cash-flow positive, we're at the inflection point — proven product, paying customers, and a team ready to scale. The next chapter is building the engineering organization that can take us there.

Requirements

10+ years of software engineering, with the last 4–6 in engineering leadership roles at high-growth startups or scale-ups
Proven track record of building and scaling engineering teams — you've grown a team through a funding transition and have the org design scars and wins to show for it
Deep technical credibility — you can hold your own in a system design conversation with a Staff Engineer, review a PR meaningfully, and debate architectural trade-offs without deferring to someone else's opinion
Hands-on platform engineering background — you understand distributed systems, API design, cloud architecture, and the operational realities of running production AI systems at scale
AI engineering fluency — you know how LLMs work, how to build reliable systems on top of them, and what "agentic AI" means at the infrastructure level, not just the product level
Experience with TypeScript, Python, or both at a production scale; comfort across the backend/frontend divide
Exceptional people leadership — engineers want to work for you, not just with you; you develop talent, resolve conflict cleanly, and make hard calls without drama
Strong product intuition — you understand why decisions get made, not just what was decided; you can push back on the roadmap and propose better paths without losing the relationship
BS/BA in Computer Science required; MS or PhD in CS, AI, or related field is a strong plus
San Francisco-based; committed to 5 days/week in the office — this is a culture and collaboration role as much as a technical one

Nice To Haves

Domain experience in conversational AI, voice AI, or agentic systems at production scale
Background in fintech, banking, or regulated financial services
Experience as a VP or Director of Engineering at a company that went from Series A to Series C+
Track record of building globally distributed engineering teams (US + international)
Published writing, conference talks, or open-source contributions in AI systems, platform engineering, or engineering leadership

Responsibilities

Engineering organization design and growth — build a world-class team from ~10 to 50+ engineers globally, including hiring for Staff, Senior, and mid-level roles across platform, AI, and frontend
Technical vision and architecture — partner with Bruce Kim to define the platform strategy, drive architectural decisions, and ensure our systems scale with our ambitions
Engineering velocity and quality — establish the processes, tooling, and culture that let teams ship fast without accruing technical debt that kills momentum
Cross-functional leadership — serve as the engineering voice in product, design, and executive conversations; translate business priorities into engineering roadmaps and vice versa
Agentic AI and conversational AI platform — own the evolution of the platform layer that powers our real-time AI products, including LLM orchestration, retrieval systems, and integration infrastructure
AI-native engineering culture — embed AI development tools (Claude Code, Cursor) as standard practice, and build a team fluent in building on top of frontier AI models
Recruiting and talent brand — make interface.ai the place top SF engineers want to work; own the technical interview process, the offer strategy, and the employer brand in the market
Eng/Ops excellence — drive incident response, observability, reliability targets, and the engineering rituals (planning, retrospectives, tech reviews) that keep a fast-moving team aligned