Staff Product Manager, Agentic Experiences

Responsibilities

• Own the agent's experience of Deepgram across its lifecycle — discovery and recommendation, integration and onboarding, production use, and verification.
• Stand up a system that measures and optimizes every stage of the funnel for agents, and keep it current as agent behavior changes.
• Own the product surfaces specific to the agent experience: signup and authentication, the trial-key and token defaults and programmatic key provisioning, console onboarding, and the verification tooling that lets an agent confirm its integration is actually correct.
• Set the requirements for what the agent experience needs from the shared developer platforms — SDK ergonomics, the agent-readable documentation and llms.txt, the MCP server, the CLI, the skills package, and starter templates — and prototype the changes directly, in partnership with the team that owns those platforms.
• Stand up the operating system your work runs on — the rhythms of business, data-driven optimization, and the experimentation platform — by building it in-house or by researching and deploying the best tools available.
• Turn the scale of agent traffic into fast feedback loops, so the product improves as agents use it.
• Bring the product's point of view on agents as users: what they need, where they fail, and what to change, grounded in how models actually retrieve, choose, and integrate.

Requirements

• Excellent product management judgment. You own product and roadmap, set direction, decide under uncertainty, ship outcomes, and lead cross-functional work without authority. You can show the results.
• A former engineer's depth (required). You were a senior software engineer, or more, before you moved to product. You architect and ship production systems, you read and write real code, and you reason with engineering at their level.
• Deep AI fluency, proven by shipped work (required). You have personally built and shipped AI software that goes well beyond prompt files and markdown — agents, MCP servers, CLI tools, agent and evaluation harnesses, real model-integrated tools — and it is public.
• Proven ability to stand up a complete system from scratch — the rhythms of business, the reporting and optimization, the experimentation platform — yourself or in-house, or by researching and deploying the right tools.
• PLG and developer-product fluency. You understand how developers, and increasingly their agents, adopt APIs, and you understand product-led growth.
• The judgment to distrust a number or a passing test before you build on it. You ask whether it is real, as a reflex.
• Clear communication with executives: you lead with the decision, keep your method in reserve, and hold up under pushback without either caving or digging in.

Nice-to-Haves

• Built specifically for AI agents as the consumer — MCP servers, agent harnesses, CLI tools, agent-readable docs, tool definitions, or evals for agent output.
• Experience with voice, audio, or real-time streaming systems.
• A track record of open-source work with real adoption.
• Time in a company with both a self-serve and an enterprise motion.