Suggested rewrite: Led a cross-functional initiative that improved [business outcome] by [measurable result], demonstrating experience relevant to this role...
Staff Product Manager (Product-Led Growth)
- Remote from
- USA
- Salary
- USD 215k–268k / yr
- Department
- Product & Operations
- Employment
- Full Time
- Experience
- Director
- Published
- Apply before
- 7 Dec 2026
- Listing views
- 28
- Application actions
- 1
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About this role.
Company Overview
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.
Company Operating Rhythm
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
We are hiring a Staff Product Manager focused on product-led growth. You will own the growth of the developer’s journey from first touch to committed customer — as much of that funnel as the team’s size calls for — and you will build the product that moves it. This is a founding-era team: the systems are young, the questions are not handed to you, and the person in this role defines what good looks like rather than executing someone else’s definition. You report to the VP of Self-Serve and partner daily with engineering, design, developer relations, and the product analyst who owns the measurement behind your work.
What You’ll Do
– The growth of the PLG funnel — the metrics, the experiments that move them, and the product changes behind them. Your span may be one part of the funnel or the whole of it, and it can grow or narrow as the team does.
– The product across that experience — onboarding, the console, and the activation path a developer travels. You decide what ships and why; engineering builds it, and the developer-experience team owns the SDKs and docs you build on.
– The complete system your area runs on, stood up from scratch: the rhythms of business, data-driven optimization, and the experimentation platform. You build it yourself or in-house, or you research and deploy the best tools available.
– A weekly rhythm of shipping and measuring: a hypothesis, a change, an honest read of whether it worked, and the next bet. Not a roadmap of features shipped on faith.
– The cross-team work that falls between owners: with developer relations on acquisition and the shared developer platforms, with marketing on search and answer-engine presence, with sales and customer success on the handoff, and with the analyst on definitions and instrumentation.
You’ll Love This Role If You
– Get energized by being early — building the practice, not inheriting it.
– Have strong, evidence-backed opinions about product-led growth and are eager to be proven wrong by the data.
– Build things yourself. You prototype, you write the SQL to check a hunch, you stand up the landing page, you ship the fix. You do not wait for someone to build it for you.
– Reason at the altitude of a developer making an integration choice, increasingly with an AI agent in the loop.
– Are genuinely AI-native: you work with these tools all day, you know where they help and where they mislead, and you turn them on your own problems.
It’s Important to Us That You Have
– Product-led growth experience. This is mandatory. You have owned a PLG funnel metric — activation, conversion, retention, or acquisition — and moved it, in a product real users adopted without a salesperson.
– A track record as an all-around excellent PM: you set direction, you decide under uncertainty, you ship, and you can show the outcomes.
– The instinct and ability to build. You are technical enough to prototype, to reason about engineering tradeoffs, and to do your own analysis rather than wait on someone else’s.
– 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.
– Genuine AI fluency, judged by what you produce, not what you claim. You have built AI into your workflow past the first demo, you verify what it gives you, and you have a point of view on where it is still wrong.
– The judgment to distrust a number before you build on it. You ask whether a metric is real or an artifact of how it was measured, as a reflex.
– Clear communication with executives: you lead with the decision, hold your method in reserve, and hold up under pushback without either caving or digging in.
It Would Be Great If You Had
– Experience with a developer or API product, and fluency in how developers adopt SDKs and APIs.
– A view on how AI coding agents change discovery and integration, and what it means to have an agent as your user.
– A computer-science or engineering background, or a history of shipping your own side projects.
– Experience with the tools we use — Superset, Heap, GrowthBook, and the like.
– Time in a company that ran both a PLG and an enterprise motion, and understood how they feed each other.
Notice: We’re aware of individuals impersonating Deepgram recruiters. All legitimate Deepgram recruiting communication comes from an @deepgram.com email address. If you’ve received a message claiming to be Deepgram, please forward it to careers@deepgram.com.
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Sample interview questions
I would explain the funnel stage, baseline metric, user segments and evidence behind the main friction hypothesis. I would then describe the experiment, implementation, measured lift, statistical or practical validation, and the follow-up iteration or rollout decision.
I would first validate the event definitions, tracking completeness and cohort comparability before interpreting the decline. Then I would segment by acquisition source, API use case, SDK, account type and onboarding step, combine behavioral data with developer feedback, and prioritize a testable intervention at the largest verified point of friction.
I establish a clear north-star outcome and a small set of leading metrics, then create a hypothesis backlog ranked by expected impact, confidence, effort and learning value. I favor small, instrumented releases that resolve important uncertainty quickly while building reusable capabilities such as reliable event tracking and experimentation workflows.
I use AI to accelerate synthesis, prototyping, query drafting and exploration, but I verify outputs against source data, user research and technical constraints. I treat AI-generated conclusions as hypotheses, document assumptions, and retain human accountability for product decisions.
I lead with the decision and its expected business impact, then summarize the strongest evidence, key assumptions and alternatives considered. I invite challenge on the assumptions, explain the cost of delaying or choosing another path, and clearly state what data would cause me to revise the recommendation.
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