Responsibilities

• Own the voice agent category narrative. Define what a production-grade voice agent platform is in a way that plays to Deepgram's strengths: one owned, end-to-end stack with sub-300ms latency, barge-in, and turn-taking.
• Build and execute GTM strategy across developer adoption, pipeline influence, and account expansion. Voice agents are where STT, TTS, and LLM orchestration come together; own the full-stack story while knowing how the pieces show up in broader deals.
• Lead product launches for Voice Agent releases: new conversational capabilities, latency and quality advances, BYO model support, deployment and compliance options. Clear messaging, sharp launch plans, tight cross-functional execution.
• Own positioning across the build spectrum. Understand the different approaches teams take to building voice agents, from DIY frameworks to managed platforms and enterprise suites, and build the battlecards, objection-handling guides, and build-vs-buy narratives that show why the unified-API approach wins for production. Many players here are partners who embed Deepgram as their speech layer.
• Enable sales and build proof points. Collateral, customer stories, benchmarks, and TCO narratives that win voice agent deals and stand up to technical and economic scrutiny.
• Collaborate with Product on market and competitive intelligence: what voice agent builders need, what the market is shipping, where it's going.
• Drive developer-facing content and awareness with Developer Relations: tutorials, documentation messaging, reference architectures, use case content, and SEO tied to how voice agent builders actually search.
• Build AI-assisted PMM workflows. Use AI to accelerate research, competitive synthesis, and content production. Build repeatable systems, not one-off prompts. You set the strategy; AI handles the first drafts.

Requirements

• 5+ years of product marketing experience, including at least 2 years on infrastructure, API, or developer-facing products.
• Strong messaging skills: you can build a positioning architecture from scratch and know the difference between a message that's technically accurate and one that actually moves people.
• A high bar for quality: you notice when copy is off, when a layout doesn't work, when creative doesn't match the brand, and you can give useful feedback on all of it.
• Working knowledge of the AI landscape: frontier models, major LLM providers, how the ecosystem fits together. You need to talk about this credibly with technical buyers.
• Technical depth: you can engage with engineering and product teams, read API docs, and translate product-level details into market-facing narratives without oversimplifying.
• Fluency in both product-led and sales-led growth motions, and how they interact.
• Demonstrated AI fluency: concrete examples of AI-assisted workflows you've built, iterated on, and measured, with specific outcomes to back it up.

Preferred

• Experience in the voice AI space and familiarity with how STT, TTS, and LLMs come together in a production voice agent stack, and with the range of ways teams build voice agents, from DIY frameworks to managed platforms.
• Hands-on experience with AI-native tools across the stack: agentic coding (Claude Code, Codex) and collaborative work tools (Cowork, etc.).
• Experience with technical proof points, model benchmarks, or evaluation frameworks. In voice agents, buying decisions often come down to a live conversation that either feels natural or doesn't.
• Comfort operating where the playbook doesn't fully exist yet and you're building it as you go.