About The Role

Deepgram builds the models and APIs that put voice agents into production at scale. What decides whether those agents feel human is not a screen. It is whether the agent knows when you have finished speaking, what it does when you cut it off, how it recovers from a misheard word, how it confirms something consequential before acting, and how it covers the milliseconds it cannot remove.

Today those decisions live inside prompts and pipeline config, made ad hoc by whoever is closest to the problem. No one owns them end to end. We are hiring a Staff Conversational Designer to own them.

You will define how Deepgram's voice agents converse: persona, turn-taking, repair, confirmation, and pacing. You will work directly with ML and Engineering on the tradeoffs that make those behaviors real, including endpointing, barge-in, and latency budgets. You will build the evals that tell us whether a conversation is actually good, and publish the guidance and reference experiences that show developers how to build natural conversations on the Voice Agent API.

This is a leading edge role. Very few companies have a titled equivalent. You will be defining this discipline at Deepgram, not inheriting it.

You'll report directly to the Director of Product Design. This is a Staff IC role, and your impact shows up mostly in the leverage you create for others: the patterns our own experiences run on, and the guidance our customers build against.

Your first chapter: a documented persona and voice system, and turn-taking and repair behavior designed and shipped with Engineering. From there, scope expands into conversational-quality evals and developer-facing guidance.

What You'll Do

• Define the persona and voice system for Deepgram voice agents, and keep it coherent across experiences and use cases
• Design turn-taking, barge-in, and end-of-turn behavior with ML and Engineering, tuning responsiveness against the risk of interrupting the user, per use case
• Design conversational repair, no-match and no-input handling, and confirmation strategy, including guardrails that require confirmation before high-stakes actions
• Own latency-aware pacing and perceived responsiveness: brevity, backchanneling, hold and filler speech, all against real-time budgets
• Establish conversational-quality evals and a transcript review practice that turns production failures into a repeatable design loop
• Build the reference agent experiences and developer-facing design guidance that demonstrate best-practice conversation on the Voice Agent API
• Partner with ML and Research on ASR and TTS behavior, and on the quality criteria that define a good conversation
• Set the conversation-design principles, review standards, and shared vocabulary the broader team adopts

You'll Love This Role If You

• Believe conversation is an interface with real craft behind it, not a prompt someone tunes on the side
• Want the hard real-time problems: endpointing, barge-in, and the half-second you cannot design away
• Get energized by being foundational, defining what good means for a discipline that does not exist here yet
• Think the fastest way to raise quality is to make it measurable, then make it repeatable
• Are excited by a category still inventing its interaction patterns, where the work is invention rather than iteration

It's Important To Us That You Have

We care more about expertise and leadership than years of experience.

• Deep experience designing conversational behavior for LLM-based voice agents or assistants, not only scripted IVR flows
• Real fluency with the speech pipeline, from ASR through LLM to TTS, and a working understanding of where design decisions actually live inside it
• A track record designing turn-taking, interruption, repair, and confirmation patterns that shipped and held up in production
• Evidence of building quality measurement into the practice: evals, transcript review, benchmarks, or a structured failure-analysis loop
• Exceptional writing craft, including sample dialogs, design guidance, and documentation that others can build against
• Experience influencing engineering and ML partners on behavior they own, without authority over them
• Experience designing for developers or technical users, including APIs, SDKs, and documentation surfaces
• A working AI practice, with a point of view on where these tools help and where they mislead

It Would Be Great If You Had

• Time on a named assistant or a production voice agent platform
• Practice with Wizard of Oz testing and sample dialog methods
• Hands-on work with eval tooling for LLM or voice quality
• Experience in high-stakes or regulated conversation domains where confirmation and recovery carry real cost
• Multilingual or cross-locale conversation design experience
• Background in high-growth B2B companies with both self-serve and enterprise motions