Research , Data Science

Location

Remote, Ann Arbor, MI, San Francisco, CA

Full time

Remote

Research

• Estimated Base Salary $150K – $220K
  • Offers Equity
  • Offers Bonus
  • 10% Annual Bonus

This range is determined by work location and additional factors, including job-related skills and experience. There may be instances where a salary higher or lower than this range may be appropriate for a candidate whose qualifications differ meaningfully from those listed in the job description. Please note that the compensation details listed on US role postings reflect the base salary only and does not include bonus, equity or benefits.

Deepgram is the leading voice AI platform for developers building speech-to-text (STT), text-to-speech (TTS) and full speech-to-speech (STS) offerings. 200,000+ developers build with Deepgram’s voice-native foundational models – accessed through APIs or as self-managed software – due to our unmatched accuracy, latency and pricing. Customers include software companies building voice products, co-sell partners working with large enterprises, and enterprises solving internal voice AI use cases.

The company ended 2024 cash-flow positive with 400+ enterprise customers, 3.3x annual usage growth across the past 4 years, over 50,000 years of audio processed and over 1 trillion words transcribed. There is no organization in the world that understands voice better than Deepgram

Voice is the most natural modality for human interaction with Machines. However, current sequence modeling paradigms based on jointly scaling model and data cannot deliver voice AI capable of universal human interaction. The challenges are rooted in fundamental data problems posed by audio: real-world audio data is scarce and enormously diverse, spanning a vast space of voices, speaking styles, and acoustic conditions.

Even if billions of hours of audio were accessible, its inherent high dimensionality creates computational and storage costs that make training and deployment prohibitively expensive at world scale. We believe that entirely new paradigms for audio AI are needed to overcome these challenges and make voice interaction accessible to everyone.

Deepgram is currently looking for seasoned Data Scientists with demonstrated experience solving hard data problems while exploring research frontiers to join our Research Staff. Conversational audio presents incredibly rich scientific, engineering, and infrastructure challenges that are orders of magnitude harder than working with text. As a Member of the Research Staff, you will help us to build an industrial “data factory” that will be used to power the next generation of Voice AI systems - unlocking the creation of models that go beyond basic transcription and comprehension, capturing nuanced meanings in complex conversations, adapting robustly to diverse speech patterns, and generating empathic responses with human-like, contextualized speech.

You will collaborate closely with our product, engineering, and data teams to build and deploy models in the most scalable voice API on the planet. We look forward to you bringing your expertise, sharing insights from your latest experiments, and collaborating with us to push the boundaries of AI and voice technology.

We are seeking Research Staff who:

• See "unsolved" problems as opportunities to pioneer entirely new approaches

• Can identify the one critical experiment that will validate or kill an idea in days, not months

• Have the vision to scale successful proofs-of-concept 100x

• Are obsessed with using AI to automate and amplify your own impact

If you find yourself energized rather than daunted by these expectations—if you're already thinking about five ideas to try while reading this—you might be the researcher we need. This role demands obsession with the problems, creativity in approach, and relentless drive toward elegant, scalable solutions. The technical challenges are immense, but the potential impact is transformative.

• Drive high performance data acquisition, preparation and synthesis pipelines to generate data for the next…