Member of Technical Staff, Backend

Vapi (/ˈVɑːpi/): Voice AI that resolves, not transfers Powering 1 billion calls for companies like Amazon Ring, Intuit, ServiceTitan, and New York Life Trusted by 1 million developers building the future of voice agents Backed by Peak XV, Bessemer, Kleiner Perkins, M12, Y Combinator, and more with $72M raised Try talking to Vapi now! Why We’re Hiring This Role: More than 1 million developers have built 2.7M+ agents on Vapi. Every assistant depends on a growing product surface—from Composer, Simulations, Monitoring, and Knowledge Base to Versioning and Deployments. Behind those experiences are hard backend product problems: durable APIs and data models, real-time and asynchronous workflows, observability, evaluations, guardrails, and reliable operation of non-deterministic systems. We’re hiring a backend product engineer to own those systems end to end and help customers move voice agents from a compelling demo to dependable production—at Fortune 500 scale. The work is genuinely hard and unusually fun. What You’ll Do: 30 Days: Make real calls and build real assistants across LLM, speech-to-text, text-to-speech, and telephony integrations. Develop a working mental model of how Vapi’s product surfaces map to APIs, services, data systems, and production workflows, then ship your first scoped backend product improvement. 60 Days: Own a meaningful backend area in agent se [... source excerpt omitted ...] evaluation and rollout criteria; and instrumenting the result. Use AI coding agents thoughtfully as a throughput and quality lever. 90 Days: Operate as an independent owner for a customer-facing backend area. Run the full improvement loop—authoring, evaluation, deployment, observation, and iteration—while improving reliability and developer experience. Be a core voice in product decisions and have multiple meaningful improvements in production. Who You Are: Must-haves A track record operating at senior or staff scope: independently owning customer-facing backend product areas from an ambiguous problem through measurable production outcomes. Strong hands-on experience with Ty [... source excerpt omitted ...] s with databases, queues, workflows, or event-driven patterns—and reasoning clearly about failure modes. Strong product judgment: you start with the user problem, turn incomplete requirements into a clear technical approach, and make tradeoffs based on customer impact. Production ownership: you care about testing, observability, reliability, incident response, safe rollouts, and what happens after launch. Regular, thoughtful use of AI-assisted development tools to improve speed and quality, paired with strong engineering judgment about the result. Self-direction, customer empathy, and clear communication. You stay hands-on, collaborate directly across functions, and remain accoun