Staff Product Manager, Agentic Experiences (Former Engineer)

Deepgram is the leading platform underpinning the emerging trillion-dollar Voice AI economy, providing real-time APIs for speech-to-text (STT), text-to-speech (TTS), and building production-grade voice agents at scale. More than 200,000 developers and 1,300+ organizations build voice offerings that are ‘Powered by Deepgram’, including Twilio, Cloudflare, Sierra, Decagon, Vapi, Daily, Cresta, Granola, and Jack in the Box. Deepgram’s voice-native foundation models are accessed through cloud APIs or as self-hosted and on-premises software, with unmatched accuracy, low latency, and cost efficiency. Backed by a recent Series C led by leading global investors and strategic partners, Deepgram has processed over 50,000 years of audio and transcribed more than 1 trillion words. There is no organization in the world that understands voice better than Deepgram.

At Deepgram, we expect an AI-first mindset—AI use and comfort aren’t optional, they’re core to how we operate, innovate, and measure performance.

Every team member who works at Deepgram is expected to actively use and experiment with advanced AI tools, and even build your own into your everyday work. We measure how effectively AI is applied to deliver results, and consistent, creative use of the latest AI capabilities is key to success here. Candidates should be comfortable adopting new models and modes quickly, integrating AI into their workflows, and continuously pushing the boundaries of what these technologies can do.

Additionally, we move at the pace of AI. Change is rapid, and you can expect your day-to-day work to evolve just as quickly. This may not be the right role if you’re not excited to experiment, adapt, think on your feet, and learn constantly, or if you’re seeking something highly prescriptive with a traditional 9-to-5.

The Opportunity

The way developers find and adopt an API is changing. More and more, an AI coding agent discovers us, chooses the provider, writes the integration, and consumes our API, often with no human ever at the console. Deepgram is looking for a Staff Product Manager to own our product experience for that agent across its whole life with us: how an agent discovers and chooses Deepgram, how it integrates, how it uses the product in production, and how it verifies its own work. You will own that experience end to end, and you will build the system that keeps improving it as agent behavior changes. You report to the VP of Self-Serve.

This is a product management role in the conventional sense: you own the product, its direction, and its decisions, and engineering builds it. Two things set the role apart, and both are required — you are a former engineer who still builds to think and to prove a point, and you are deeply AI-native, with shipped work to show for it. You will prototype, read and write code, and reason with engineering at depth; your job is to own the product, not to be its implementing engineer.

What You'll Do

- 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.

You'll Love This Role If You

- Were an engineer, moved to product to own outcomes, and never stopped building.

- Think like an architect and can design and stand up a self-optimizing system across discovery, onboarding, and integration.

- Have felt, first-hand, how an AI agent succeeds or fails at a real integration, and have strong opinions about why.

- Want to own a product that is becoming the front door of the business, at the moment it is becoming that.

- Are energized by being early — defining the practice, not inheriting it.

It's Important to Us That You Have

- 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. You are not a vibe coder who assembles what a tool generates.

- 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. Send us the GitHub; we will read the code, the commits, and the design.

- 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.

It Would Be Great If You Had

- 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.

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