Suggested rewrite: Led a cross-functional initiative that improved [business outcome] by [measurable result], demonstrating experience relevant to this role...
Director of Research, Text-to-Speech
- Remote from
- USA
- Salary
- USD 288k–361k / yr
- Department
- Data Science & Analytics
- Employment
- Full Time
- Experience
- Director
- Published
- Apply before
- 10 Dec 2026
- Listing views
- 30
- Application actions
- 2
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About this role.
Company Overview
Deepgram is bringing the world’s most natural interface, voice, to the technology we use every day. Our speech models and real-time infrastructure power AI systems that keep up with the way people actually speak. More than 5,000 organizations run conversational voice AI on Deepgram in production, including Twilio, Cloudflare, Sierra, Decagon, Vapi, Cresta, Granola, and Jack in the Box.
The Deepgram Voice Agent Platform brings together speech-to-text, text-to-speech and speech-to-speech, with voice-native foundation models available through cloud APIs or self-hosted and on-premises deployments. Built for accuracy, low latency and cost efficiency, Deepgram makes high-quality voice experiences practical to build and run at scale. More than 650,000 developers have used Deepgram to process more than 200,000 years of audio. Every day, more of the world’s conversations with AI run on our models.
AI-First by Default
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. If you’re energized by experimenting, adapting, and learning constantly, you’ll thrive here. Expect your day-to-day to evolve as fast as the technology does.
The Opportunity
We’re looking for a Director of Research to own our Text-to-Speech program end to end — the research strategy, the technical bets, and the models that ship. This is a hands-on leadership role: you set direction and stay in the details, shaping architectures, experiments, training strategy, and evaluation. You’ll also build the team and operating model that let exceptional researchers move fast without lowering the bar.
The mandate is broad: advance the state of the art in speech generation, and turn those advances into models in production.
What You’ll Do
Own the TTS research and model roadmap — decide which technical directions can materially move speech-generation quality, including the expensive and non-obvious ones, and recognize when an approach should change or die.
Drive advances across neural audio modeling, prosody and expressiveness, controllability, multilingual speech, voice identity and consistency, data and training strategy, post-training, and inference performance — and make sure they land as measurable production gains.
Stay deeply technical: review research, challenge assumptions, design experiments, diagnose model failures, and take on the highest-leverage problems yourself.
Build evaluation and benchmarking that explains why models improve, not just whether they did — automated metrics alongside human perceptual assessment.
Lead a mix of individual contributors and tech lead managers. Hire and develop both, hold an exceptionally high technical bar, grow senior researchers into technical leaders, and set direction across sub-teams while pushing decisions down to the people closest to the work.
Partner with engineering and product leadership on ship-readiness, and represent Deepgram’s TTS research internally and externally.
You’ll Love This Role If You
Want to own a frontier-model program end to end rather than optimize one slice of someone else’s agenda.
Care about the breakthrough and the ship in equal measure.
Still love training runs, failed evals, and architecture arguments, even as most of your impact now comes through the people you lead.
Get energy from deciding which bets are worth making when the answer isn’t obvious and the experiments are expensive.
You Must Have
Deep expertise in modern TTS, speech generation, or audio generative modeling, with a track record of personally training and improving large-scale neural models.
Command of the modern speech-generation stack and the open problems behind naturalness, expressiveness, controllability, robustness, voice consistency, and inference cost.
A history of setting research direction under genuine uncertainty: prioritizing experiments, allocating compute and researcher time, and killing approaches that aren’t working.
Experience leading researchers and research engineers through other technical leaders — developing tech lead managers or equivalent, setting direction across sub-teams — while staying technically influential yourself.
AI as your default mode of work, not an occasional tool. You’ve rebuilt how you and your team operate around it, and you have a specific, earned view of what it still can’t do in speech research.
The ability to make complex technical tradeoffs legible to product, engineering, and executive audiences.
It’d Be Great If You Had
TTS or generative-audio models deployed at meaningful production scale.
Built or substantially scaled a high-performing AI research organization.
Sophisticated evaluation systems for generative speech; expressive or multilingual generation, voice cloning and adaptation, or controllable generation.
Recognized external contributions — publications, open source, patents, invited talks — in speech synthesis, neural audio codecs, speech language models, or multimodal models.
Experience in fast-moving startup or research environments that routinely take models from idea to production.
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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Role DNA
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Core skills
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Sample interview questions
I would begin with a clear quality and cost baseline using automated and human evaluations, then identify the highest-impact gaps in naturalness, controllability, robustness, and latency. I would organize a portfolio of incremental production improvements and higher-risk research bets, with explicit success criteria, compute budgets, and regular decision points to scale, redirect, or stop experiments.
I would combine objective metrics with carefully designed listening studies across representative languages, speakers, prompts, and acoustic conditions. Beyond aggregate scores, I would inspect failure slices such as prosody, pronunciation, identity consistency, long-form stability, and latency, then validate that improvements persist in production-like serving conditions.
I make uncertainty explicit by defining hypotheses, measurable milestones, and time- or compute-bounded experiments. Teams should have autonomy to pursue promising directions, but they also need transparent review mechanisms so that negative results become useful evidence and unproductive approaches are ended quickly.
The decision must balance perceptual quality and expressiveness against latency, serving cost, reliability, safety, and operational complexity. I work closely with engineering and product to establish launch thresholds, optimize inference where possible, and ensure the evaluation suite reflects real customer use cases rather than only research benchmarks.
I would give them ownership of meaningful research areas, require clear technical narratives and decision documents, and coach them on setting priorities across collaborators. Regular feedback should address both research rigor and leadership behaviors, including how they create leverage for others, communicate tradeoffs, and turn results into shipped impact.
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