Scan your resume against ATS criteria for this Director, Text-to-Speech Synthesis Research role at Deepgram.
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.
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.
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
Own the TTS research and model roadmap — decide which technical directions can materially move speech-generation quality, including the expensive and non-obvious, 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.
Want to own a frontier-model program end to end rather than optimize 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.
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.
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.
Own the TTS research and model roadmap — decide which technical directions can materially move speech-generation quality, including the expensive and non-obvious, 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.
Want to own a frontier-model program end to end rather than optimize 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.
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.
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.
How to apply for Director, Text-to-Speech Synthesis Research at Deepgram?
Click the "Apply on Company Website" button on this page to submit your application directly on the employer's official portal.
What is the salary for this role?
Salary details will be discussed during the interview.
What experience is required?
Fresher (0-1 yr) of experience is required.
Is this position still open?
Yes, currently active and accepting applications.
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Director, Text-to-Speech Synthesis Research
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