Deepgram

Voice AI Platform — Powering the Voice AI Economy

The consensus on Deepgram: Choose Deepgram if you want to work on hard speech and audio AI problems with kind, talented people — but expect intensity when the CEO sets the pace.

2015
Founded
San Francisco
Headquarters
~260
Employees
$1.3B
Valuation (Jan 2026)
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Culture Overview

What it's really like to work at Deepgram

Founded in 2015 by Scott Stephenson, Adam Sypniewski, Magesh Swaminathan, and Noah Shutty, Deepgram builds the real-time API platform underpinning the voice AI economy. Over 1,300 organizations rely on Deepgram for speech-to-text, text-to-speech, and voice agent capabilities. The culture is deeply technical — researchers and engineers tackle challenging problems across audio and text domains in a collaborative, low-ego environment. After raising $130M Series C at a $1.3B valuation in January 2026, the company is scaling fast while trying to preserve the small-team feel that employees consistently praise.

Glassdoor Ratings

Deepgram Glassdoor Ratings & Employee Reviews

4.6
Overall Glassdoor Score
Based on 46 reviews
Culture & Values 4.3
Work-Life Balance 4.3
Career Opportunities 4.5
Overall Rating 4.6
88% Recommend to a Friend
90% CEO Approval
Employee Review Themes

Working at Deepgram: Pros & Cons

What employees love

  • Collaborative, humble team — talented people who genuinely support each other
  • Challenging research across audio and text AI domains — real learning opportunities
  • High trust — few guardrails, empowered to do great work autonomously
  • Already profitable with strong client base — freedom to scale without desperation

What could be better

  • Intense workload — work hard and balance can be hard to find
  • CEO-driven decisions can override team discussions and consensus
  • Public Slack call-outs framed as "just asking questions" can feel harsh
  • Post-layoff concerns — longer hours and PTO use discouraged
Engineering Culture

How the engineering team works

Core Product

Real-time speech-to-text, text-to-speech, and voice agent APIs. Powers 1,300+ organizations including enterprises building voice AI applications at scale.

AI & Research

Proprietary speech recognition models trained on massive audio datasets. Research spans audio processing, NLP, and low-latency inference optimization. Try the API →

Tech Stack

Python Rust CUDA PyTorch

Team Structure

~260 people with engineering and research as the core. Cross-functional teams working on speech, language, and infrastructure. Researchers tackle problems across audio and text domains with high autonomy.

Open Positions

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Explore open roles at Deepgram below.

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