Director, Text-to-Speech Synthesis Research

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 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.