VP, Head of Engineering

interface.ai•Palo Alto, CA

34d•$400,000 - $500,000•Onsite

About The Position

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. We're looking for a Vice President of Engineering who has been here before — not just managed engineers, but built an engineering organization from the ground up during a period of real growth. Someone who has scaled a technical team through a Series A → B → C transition, maintained quality while moving fast, and emerged on the other side with a functioning, high-trust engineering culture. You'll partner directly with our Co-Founder Bruce Kim on technical strategy and architecture, work closely with CEO Srinivas Njay on product vision and company direction, and own the end-to-end engineering function. You'll manage existing engineers, build out the team aggressively over the next 12–18 months, and set the standards that will define how interface.ai ships software for years to come. This is a player-coach role at the start. You'll be in the architecture discussions, the code reviews, and the late Friday PRs — and you'll also be building the org, running the recruiting pipeline, and presenting engineering strategy to the board. If that breadth energizes you, this is your role.

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 high-performing 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, 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