Staff Product Designer, Conversational AI

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.

Deepgram builds the models and APIs that put voice agents into production at scale. We are hiring a Staff Conversational Designer to own the decisions that make voice agents feel human: 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.

• 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

• 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

• 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

• 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

Salary range: $180K – $240K. Offers Equity. Offers Bonus.

Apply through the provided link.