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Deepgram is the leading platform for Voice AI, providing real-time APIs for speech-to-text, text-to-speech, and voice agents at scale. The company has processed over 50,000 years of audio and serves 200,000+ developers and 1,300+ organizations including Twilio, Cloudflare, and Vapi.
You will own the product experience for AI agents across their entire lifecycle with Deepgram: discovery and recommendation, integration and onboarding, production use, and verification. This is a Staff Product Manager role with a critical difference—you are a former senior engineer who still builds to think and prove points, and you are deeply AI-native with shipped work to demonstrate it.
Key responsibilities include: owning the agent's end-to-end experience with Deepgram; standing up a system that measures and optimizes every stage of the funnel for agents; owning product surfaces specific to agent experience (signup, authentication, trial keys, console onboarding, verification tooling); setting requirements for shared developer platforms (SDKs, documentation, MCP servers, CLI, starter templates) and prototyping changes directly; building the operating system for your work—business rhythms, data-driven optimization, and experimentation platforms; turning agent traffic scale into fast feedback loops; and bringing product perspective on agents as users grounded in how models actually retrieve, choose, and integrate.
You will report to the VP of Self-Serve. The role requires excellent product management judgment, the ability to own product and roadmap, set direction under uncertainty, ship outcomes, and lead cross-functional work without authority. You must have a former senior engineer's depth—you architected and shipped production systems, read and write real code, and reason with engineering at their level. Deep AI fluency is required, proven by shipped work beyond prompt files: agents, MCP servers, CLI tools, evaluation harnesses, and real model-integrated tools with public GitHub repositories.
Deepgram operates with an AI-first mindset where every team member actively uses and experiments with advanced AI tools. The company moves at the pace of AI with rapid change; expect your day-to-day work to evolve quickly. This role is ideal if you were an engineer who moved to product to own outcomes and never stopped building, think like an architect, have felt firsthand how AI agents succeed or fail at real integrations, and want to own a product becoming the front door of the business at the moment it is becoming that.
