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Deepgram is the leading platform for Voice AI, providing real-time APIs for speech-to-text (STT), text-to-speech (TTS), and voice agents at scale. The company has processed over 50,000 years of audio and transcribed more than 1 trillion words, serving 200,000+ developers and 1,300+ organizations including Twilio, Cloudflare, and Vapi.
In this Research Staff role, you will own customer problems end-to-end, turning vague challenges into measurable improvements shipped to production within weeks. You'll work on high-impact projects such as expanding models to new languages and regions, improving TTS pronunciation of names and numbers, isolating speaker voices from background noise, releasing new voice capabilities like steerable and cloned voices, and publishing benchmarks for Voice AI use-cases.
Your day-to-day responsibilities include:
- Identifying where Deepgram's models fall short and setting improvement targets
- Designing evaluations that demonstrate model improvements and explain why they work
- Curating and generating training data, fine-tuning models, and experimenting with external models
- Building working systems quickly and shipping them to production
- Collaborating with customers to gather feedback on new capabilities and guide research methodology
Deepgram operates with an AI-first mindset. All team members are expected to actively use and experiment with advanced AI tools, integrate them into daily workflows, and continuously push technological boundaries. The company moves at the pace of AI, with rapid change and evolution in day-to-day work. Success requires comfort with experimentation, quick adaptation, and continuous learning.
REQUIREMENTS:
- Deep experience training, fine-tuning, and evaluating deep learning models
- Strong Python and PyTorch skills
- Demonstrated track record of shipping ML systems to production
- Ability to own problems end-to-end and learn required skills along the way
- Good judgment about problem prioritization and production readiness
- No specific degree required; hard problems solved, shipped systems, or open source work valued
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
