Research Staff, Applied Voice AI
Deepgram · USA - Remote
About this role
## Research Staff, Applied Voice AI ### Company Overview Deepgram is the leading platform underpinning the Voice AI economy, providing real-time APIs for **speech-to-text (STT)**, **text-to-speech (TTS)**, and production-grade **voice agents** at scale. Deepgram’s voice-native foundation models are available via cloud APIs or self-hosted/on-premises software, delivering high accuracy, low latency, and cost efficiency. ### Operating Rhythm - **AI-first mindset**: AI use and comfort aren’t optional—they’re core to how teams operate, innovate, and measure performance. - **Experiment and adapt quickly**: actively use and experiment with advanced AI tools, and continuously push what these technologies can do. - **Move at the pace of AI**: expect rapid change and evolving day-to-day work. ### The Opportunity You’ll help build the engine that solves customer challenges at scale—when customers hit a limit in voice AI, you’ll **measure it, fix it, and ship the fix to every customer in weeks**. **Example projects:** - Expanding models to new **languages and regions** - Improving TTS **pronunciation** of names, terms, and numbers - Isolating a speaker’s voice from **background noise and crosstalk** - Enabling early access to new voice capabilities (e.g., **steerable and cloned voices**) and using feedback to guide research - Releasing new **benchmarks** for Voice AI use-cases ### The Role You’ll thrive if you enjoy: - Turning vague customer problems into **clear metrics** - Using whatever works to fix them (e.g., **fine-tuning, new data, external models**) - Shipping work that customers can use within **weeks** ### The Challenge We’re seeking researchers who: - **Own a customer problem** until it measurably improves in production - Find the **fastest path to a fix**, even with novel approaches - Know when a result is **ready for customers** ### What You’ll Do - Identify where models fall short and set improvement targets - Design evaluations to show whether a model is better—and why - Curate and generate data; fine-tune models - Build working systems quickly and ship them to production ### Requirements / What to Show Us - Deep experience training, fine-tuning, and evaluating deep learning models - Strong **Python** and **PyTorch** skills - Track record of **shipping ML to production** - Ability to own problems end-to-end and learn what’s needed - Good judgment on which problems matter and when results are ready to ship **Strong candidates may have:** - Experience with **speech-to-text**, **text-to-speech**, or **voice agents** - Experience with **distributed training** - **Published papers**, open source contributions, or public benchmarks ### Education No specific degree required. Show us hard problems you’ve solved, systems you’ve shipped, or open source work. ### Safety Notice Deepgram is aware of individuals impersonating recruiters. Legitimate recruiting communication comes from an **@deepgram.com** email address. If you receive a message claiming to be Deepgram, forward it to **careers@deepgram.com**.
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