Staff Product Manager, Agentic Experiences (Former Engineer)

USA | Remote

Remote

Senior

Full Time

15 days ago

💰$ 200,000 - $ 268,000

AIproduct managementformer engineerstaff levelremote

Requirements

•Excellent product management judgment with ownership of product and roadmap, setting direction, deciding under uncertainty, shipping outcomes, and leading cross-functional work without authority.
•Former senior software engineer or more before moving to product, able to architect and ship production systems, read and write real code, and reason with engineering at their level.
•Deep AI fluency proven by shipped work beyond prompt files and markdown — including agents, MCP servers, CLI tools, agent and evaluation harnesses, real model-integrated tools, with public code available.
•Proven ability to stand up a complete system from scratch — rhythms of business, reporting and optimization, experimentation platform — either personally or by researching and deploying the right tools.
•Fluency in product-led growth (PLG) and developer-product understanding, especially how developers and their agents adopt APIs.
•Judgment to distrust numbers or passing tests before building on them, questioning their reality as a reflex.
•Clear communication with executives, leading with decisions, keeping methods in reserve, and handling pushback without caving or digging in.

•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.

•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.