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Director, Text-to-Speech Synthesis Research

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Remote from
UK
Salary
Undisclosed
Employment
Full Time
Experience
Director
Published
Apply before
20 Nov 2026
Listing views
22
Application actions
1
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AI Summary

The role, at a glance.

Deepgram is seeking a hands-on research director to lead its text-to-speech program from research strategy through production deployment. The role owns technical bets across neural audio modeling, prosody, multilingual generation, controllability, voice consistency, training, and inference efficiency. The director will lead researchers and research-engineering leaders while remaining deeply involved in model design, experiments, evaluations, and failure analysis. Success requires translating frontier speech-research tradeoffs into measurable product gains and communicating them clearly to engineering, product, and executive stakeholders.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

5/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

5/5
IndependentCollaborative
AI insightThis is a senior frontier-AI leadership role requiring rare depth in large-scale speech generation as well as the ability to set research direction under substantial technical and investment uncertainty. It combines high-stakes people leadership with direct responsibility for production-quality model outcomes.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Market rate
$250,000
US market range (per year)$210k–$330k
AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer compensation is disclosed. The yearly USD figures are AI estimates for a director-level, hands-on TTS research leader in the US market and are not verified employer pay.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you prioritize competing research directions for a TTS roadmap with limited compute and researcher capacity?

I would begin with a clear product-quality north star and decompose it into measurable gaps, such as naturalness, latency, speaker consistency, multilingual coverage, and controllability. I would rank bets by expected impact, technical feasibility, cost, time to signal, and strategic differentiation, then fund a balanced portfolio of near-term improvements and higher-risk research. Short experimental milestones and explicit stopping criteria would allow us to reallocate resources quickly.

Describe an evaluation framework for determining whether a new TTS model is ready for production.

I would combine automated benchmarks with carefully designed human listening studies. Automated measures should cover latency, stability, pronunciation, speaker similarity, intelligibility, and robustness across representative languages and inputs; human evaluation should measure naturalness, expressiveness, consistency, and preference. Production readiness also requires regression testing, safety and misuse checks, cost analysis, and validation on real customer-like traffic.

How do you balance hands-on technical work with leading researchers and technical managers?

I focus my direct technical involvement on architecture decisions, difficult failure modes, evaluation standards, and the most consequential research bets. I set clear goals and decision principles, then delegate ownership to leaders closest to the work while maintaining regular technical reviews. This preserves deep context without becoming a bottleneck and helps senior researchers grow into independent technical leaders.

What approach would you take when a promising speech-generation model improves offline metrics but performs poorly in human perceptual tests?

I would treat the discrepancy as evidence that the evaluation stack is incomplete rather than assuming either signal is wrong. I would segment the failures by content type, speaker, language, prosodic pattern, and acoustic artifacts, review listener feedback, and run targeted ablations. The result should be revised metrics and training objectives that better predict perceived quality, alongside a decision about whether the model can be improved efficiently or should be retired.

How would you explain an expensive TTS research investment to product and executive stakeholders?

I would connect the investment to a specific customer and business outcome, such as improved retention, premium voice quality, lower inference cost, or differentiated multilingual capability. I would communicate the technical thesis, milestones, cost envelope, uncertainty, and decision gates in plain language. Regular evidence-based updates would make it clear what has been learned, what remains uncertain, and when we should scale, redirect, or stop the investment.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
Opportunity details

About this role.

Company Overview

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.

Company Operating Rhythm

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.

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.

Apply now >

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