Deepgram Tech Stack
Real-time voice AI platform for developers and enterprise agents
Deepgram operates a real-time speech-to-text and conversational AI platform built on foundation models trained across 50,000+ years of audio. The tech stack reveals a heavy infrastructure focus—Kubernetes, AWS, Terraform, NVIDIA, Groq, and Slurm for distributed ML training—paired with adoption of Databricks and Snowflake, indicating a shift toward scalable data pipelines for model training and analytics. Active hiring is engineering-driven (31 roles) with senior/leadership concentration, and projects span both core platform (ML training on HPC clusters) and vertical expansion (restaurant AI and federal integrations), suggesting they're scaling beyond developer APIs into enterprise automation.
Tech Stack 32 technologies
What Deepgram Is Building
◆Challenges
- High dimensionality computational costs
- Scarce audio data
- High-accuracy menu-aware drive-thru ordering
- Real-time analytics and operational intelligence
- Scaling deployments across large restaurant fleets
- Closing tts deals
- Enhancing support infrastructure
- Real world constraints
- Complex restaurant environments
- Increasing top-line revenue
▲Active Projects
- Building sales pipeline of new logos
- Deployment tooling and playbooks
- Joint value proposition development
- Generating meetings with marketing and sdr teams
- Full-stack integrations for federal accounts
- Large-scale distributed ml training on hpc cluster
- Reference architectures for restaurant fleets
- Internal data and ml training systems
- Scalable support infrastructure solutions
- In-restaurant hardware integration
Hiring Activity
Department
Seniority
Notable leadership hires: Edge Tech Lead