ML Ops Infrastructure Engineer
Deepgram · Engineering
- Location
- Remote, California, United States
- Workplace
- Remote
- Level
- Mid-level
- Type
- Full-time
- Pay
- $160K–$220K/yr (published by employer)
- Posted
- 7 months ago
- Source
- Ashby
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PythonKubernetesDockerTerraform
What the posting covers
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+…
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…
The Opportunity
Getting a model from a research notebook to a production API serving millions of requests is one of the hardest problems in AI. As an ML Ops Infrastructure Engineer at Deepgram, you will own the critical bridge between research and production -- building the…
What You'll Do
Design and build CI/CD pipelines specifically tailored for ML model development, validation, and deployment Architect and maintain model deployment pipelines that move models from research environments through staging to production with confidence Build A/B…
You'll Love This Role If You
Are excited by the challenge of operationalizing cutting-edge AI models at production scale Believe that great infrastructure is what turns research breakthroughs into customer value Enjoy designing systems that are automated, reliable, and self-healing Want…
It's Important To Us That You Have
4+ years of experience in MLOps, DevOps, or infrastructure engineering with a focus on ML systems Strong proficiency in Python and experience building automation and tooling for ML workflows Deep experience with CI/CD systems and building pipelines for…
It Would Be Great If You Had
Experience with model serving frameworks such as NVIDIA Triton Inference Server, TensorRT, or ONNX Runtime Background in speech, audio, or real-time media ML systems Experience with Infrastructure as Code tools such as Terraform or Pulumi Hands-on experience…
Excerpts only. The employer’s page has the complete posting.