Research Staff, Applied Voice AI

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.

Team Overview

Deepgram's speech-to-text and text-to-speech models run in production across more use cases than any team could plan for one at a time. Applied Research exists so we don't have to. We're a new, founding team sitting within Research, building an engine that resolves customer issues at scale: research that ships to real customers, then generalizes so the same class of problem never needs a person again. You'll work hands-on with customers running Deepgram in production.

Key Goals

Own applied research initiatives end to end: scope, timebox, and deliver independently.

Ship research into production for customers across voice models, improving accuracy/latency/naturalness for various customers & segments.

Turn one-off product fixes (or improvements) into general solutions.

Build the systems (evaluation, model adaptation, data generation) that let recurring issues resolve without Applied Research in the loop, cutting team-involved escalations.

Define and track production metrics that prove each shipped model works for real customers.

Shorten time from customer-reported issue to deployed fix.

At Staff scope: spot cross-customer patterns, surface root causes to leadership, and set priorities for the team's research roadmap.

Minimum Skills, Knowledge & Capabilities

Has shipped ML models into production for real customers and can show how success was measured.

Builds and trains models hands-on and writes production-quality code.

Tackles open problems with novel approaches when off-the-shelf tools fall short.

Applies practical product judgment to real constraints like data quality, latency, and cost.

Own the data pipeline Uses data well: designs evaluations and curates or generates training data.

Operates AI-native: designs Human+AI research and engineering workflows, including AI-assisted coding, as the default way of working.

Builds systems that eliminate recurring work instead of re-solving each instance.

Scopes ambiguous, customer-driven problems and delivers without close direction.

Communicates technical results clearly to researchers, engineers, and customer-facing teams.

Preferred Qualifications (nice-to-have, not required)

Speech, audio, or LLM domain work, such as ASR, TTS, or LLM evaluation.

Hands-on fine-tuning (e.g., LoRA), pre-training, or large-scale model evaluation.

Built data-generation or labeling pipelines for model training.

Ownership experience on a startup or small team.

Typically ~5–8 years (Senior) or ~8–12 years (Staff) in applied ML, or equivalent demonstrated experience; advanced degree or equivalent applied research experience.

We know the confidence gap and imposter syndrome can get in the way of meeting great candidates — so please don't hesitate to apply. We'd love to hear from you.

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 [email protected].