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MoonPay is a high-velocity fintech company building the operating system for value movement—crypto, stablecoins, tokenized assets, and emerging payment systems. The company serves 30M+ customers and 500+ ecosystem partners, operates under US licensing and international regulation (UK, EU, Canada, Australia), and has been recognized as one of Forbes' America's Best Startup Employers 2026 and ranked 2nd in Crypto Services on Fortune's inaugural Crypto 100.
PayBox is MoonPay's newest product, enabling people to transact directly through AI by connecting agents with payments, wallets, and applications without requiring users to share underlying credentials. Following strong initial traction post-launch, the team is now executing multiple major product tracks: expanding PayBox across AI platforms, building peer-to-peer payment experiences, integrating with DeFi applications, growing the plugin ecosystem, and significantly improving the end-to-end product experience.
You will join as a Staff Software Engineer with founding-engineer-level scope and autonomy. You'll work closely with the Chief Engineer and a small, highly focused team to own significant areas of PayBox end-to-end—from identifying problems and defining approaches through building, shipping, and iterating in production. Your work will span designing and building entirely new PayBox capabilities, obsessing over onboarding and UX details, building integrations across AI platforms and applications, developing infrastructure for ecosystem growth, and reasoning from first principles about difficult technical and product problems. You'll move fluidly across different parts of the product and technical stack depending on where the highest-impact problem lies, and you'll use AI extensively as a tool to increase speed and output while maintaining independent technical reasoning and accountability.
The company culture emphasizes outcomes over process, impact over titles, and hard problems with real ownership. AI is the default operating mode—you're expected to use it daily to handle manual work so you can focus on what matters. The pace is real, the bar is high, and success requires teammates who love winning and building together.
REQUIREMENTS:
Must-Have Experience and Skills:
- Exceptional technical reasoning: strong engineering fundamentals, ability to break down unfamiliar and ambiguous problems and reason toward good solutions
- High agency: you identify important problems and take responsibility for solving them without waiting for perfectly defined requirements
- End-to-end ownership: demonstrated experience building and shipping meaningful products, projects, or systems from beginning to end
- Thoroughness: deep care for work quality and attention to details others might overlook; getting something working is the beginning, not the end
- Strong systems thinking: ability to design systems, understand technical trade-offs, and make sensible architectural decisions while moving quickly
- Product judgment: understanding that great engineering includes the experience around the technology, not just the underlying implementation
- Comfort with ambiguity: you thrive in environments where there are more problems than people and priorities can change quickly
- Independent thinking in an AI-native environment: you use AI aggressively as a tool but don't outsource your reasoning to it; you understand output, challenge it, and remain accountable
- Deep motivation: you want ownership and responsibility and care about product outcomes, not simply completing assigned work
Nice-to-Have Experience:
- Experience as a founding engineer or early engineer at a high-growth startup
- Experience founding and building your own company or product
- Experience taking a product from idea through launch and subsequent iteration
- Experience operating in small, highly autonomous engineering teams
- Experience building AI-native products, agents, or developer tooling
- Experience with payments, fintech, crypto, or DeFi
- Previous experience with MCP or other emerging agent protocols
Note: The posting explicitly states that the nice-to-have list is indicative and encourages applications from candidates who feel they are a 75% match rather than requiring 100% of listed areas.
