Systems Architect AI/ML Infrastructure
Deepgram · Engineering
- Location
- Remote, California, United States
- Workplace
- Remote
- Level
- Senior
- Type
- Full-time
- Pay
- $160K–$239K/yr (published by employer)
- Posted
- 7 months ago
- Source
- Ashby
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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
Deepgram's infrastructure spans bare metal GPU clusters, multi-cloud deployments, and global edge presence -- all serving real-time voice AI at massive scale while simultaneously powering large-scale model training. As a Systems Architect, you will own the…
What You'll Do
Define and drive the end-to-end infrastructure architecture for Deepgram's AI/ML workloads across production inference and research training Design multi-cloud and hybrid infrastructure strategies that balance performance, reliability, cost, and vendor…
You'll Love This Role If You
Think in systems -- you naturally see the connections between compute, storage, network, and how they interact under load Are motivated by designing infrastructure that operates at the intersection of real-time production systems and large-scale ML training…
It's Important To Us That You Have
7+ years of experience in infrastructure engineering, systems architecture, or a senior technical role focused on large-scale infrastructure Proven experience designing multi-cloud architectures spanning AWS and at least one other major cloud provider or…
It Would Be Great If You Had
Direct experience architecting infrastructure for ML training workloads -- distributed training, large dataset management, experiment infrastructure Background in cost optimization and FinOps practices for large-scale cloud and bare metal infrastructure…
Excerpts only. The employer’s page has the complete posting.