Senior Software Engineer (Full-Stack/Backend)

About the Role

Agave is hiring a world-class senior full-stack/backend engineer to build AI and agent products on top of hard-won financial data infrastructure. Fresh off a $15M Series A led by Accel (total funding $20M+), we're experiencing hockey-stick growth and need exceptional engineers to scale with us.

Location: Remote-optional for candidates outside the Bay Area. Bay Area candidates are expected in our San Francisco office 5 days/week, with a relocation bonus available.

About Agave

We're building the financial operations platform for construction—a $12T global industry still run by 20–40 year old software. With 550+ customers, backing from Accel and Y Combinator, and investors from Procore, Autodesk, and Plaid, we've proven product-market fit and revenue traction.

Our three co-founders have worked together 12+ years, including at Graphiq (acquired by Amazon in 2017) and Amazon. We've spent five years building foundational data layers that give us unusually deep access to customers' financial and project data across fragmented legacy systems—our core moat. Now we're ramping AI-powered products on top of it. Our team is small, scrappy, and high-caliber.

Why Join?

• Early-stage yet de-risked: proven product, thousands of active users, hockey-stick revenue growth, and eliminated strategic risks (product-market fit, market validation, co-founder alignment). You get startup upside with lower risk.
• Outsized impact: work directly with customers on interesting technical challenges you won't find at larger companies. Own architectural decisions with real influence.
• Technical, experienced team: work with a founding team that's been together 10+ years, intense, and ambitious. Huge learning opportunity with genuine mentorship.
• Rare market opportunity: construction is overlooked by most startups, giving us a multi-year competitive head start. Backed by world-class investors and construction insiders.
• Accelerating momentum: AI products are the fastest-growing segment; one-third of new deals now include AI. Eight new channel partnerships signed last month alone.

Requirements

• Backend/full-stack engineer with 6+ years of professional experience
• Designed and built large-scale systems from scratch; owned them through years of production use
• Deep experience with cloud infrastructure (AWS) and distributed systems in production: caching, queuing, data consistency at scale
• Debugged hard production issues end-to-end: database performance, memory, concurrency, third-party API failures
• Comfortable across the full stack; able to learn unfamiliar or legacy systems (SQL, DLLs, etc.)
• Lived through scaling pain: 100x traffic growth, migrations, incident postmortems
• Delivered large, ambiguous projects end-to-end; can explain tradeoffs to founders and customers, not just engineers
• Self-motivated, high ownership, low ego; excited to work on a fast-paced, intense team and build world-class engineering culture
• Bay Area candidates: pumped to work in-person in SF 5 days/week. Outside Bay Area: remote-optional
• No prior experience with our specific tech stack required

What We Expect From You

• Own major subsystem architecture: new integration categories, sync engine internals, AI product infrastructure. You make and defend the design decisions.
• Set team patterns: your abstractions become how we build; influence through design docs, code reviews, and tooling
• Scope and de-risk multi-week projects: identify unknowns, prototype early, sequence work
• Make pragmatic tradeoffs: build-vs-buy, tech debt decisions with clear reasoning
• Raise the bar: mentor engineers through design reviews and pairing; shape technical culture

Technical Challenges

AI & Agents: Build agents that read, reconcile, and act on construction financial data (invoices, change orders, pay apps, budgets) inside legacy systems never designed for querying. Hard problems: grounding accuracy, evaluating quality against 30-year-old ERPs, designing tools over our APIs, and deciding deterministic vs. model-driven flows.

Unification: Unify dozens-to-hundreds of fragmented systems under a single standard we define: data objects, models, authentication, account linking, and features like filters and pagination. Foundation for AI products; every feature designed generically across current and future systems.

Compensation Disclosure

This salary range is an editorial market estimate, not provided by the employer, and may vary significantly by experience, location, equity, and other factors.