AI/ML Platform Engineer: Kubernetes & GPU Infra at Scale

Deepgram; San Francisco (CA); On-site; USD 180,000 - 260,000; Full time

Job summary

Deepgram is seeking an experienced Platform Engineer to build and operate the hybrid infrastructure foundation for AI/ML research and product development. You will architect, build, and run platforms spanning AWS and on‑premises, enabling teams to train and deploy complex models at scale.

You’ll collaborate with AI researchers and engineers to design tools, automate deployment cycles, and optimize performance and cost across cloud and bare‑metal environments.

Qualifications

• 5+ years of experience in Platform Engineering, DevOps, or SRE.
• Hands-on production infrastructure with Terraform.
• Expert-level Kubernetes knowledge in large-scale environments.
• Strong scripting and automation skills (e.g., Python, Go, Bash).
• Experience with CI/CD systems (e.g., GitLab CI, Jenkins, ArgoCD) and building developer tooling.

Responsibilities

• Architect and maintain the core computing platform using Kubernetes on AWS and on-premises, scalable environment.
• Develop and manage infrastructure using Terraform as IaC, ensuring reproducibility and automation.
• Design and optimize AI/ML job scheduling/orchestration with Slurm and Kubernetes integration.
• Provision, manage, and maintain on-premise bare metal server infrastructure for high-performance GPU computing.
• Implement networking and storage solutions to support hybrid workloads (CNI, CSI, S3).
• Develop observability stack (monitoring, logging, tracing) and automation for operations.

Skills

Kubernetes expertise

Terraform

Python

Go

Bash

CI/CD tooling

Automation

Tools

Terraform

GitLab CI/Jenkins/ArgoCD

Job description

Deepgram is seeking an experienced Platform Engineer to build and operate the hybrid infrastructure foundation for AI/ML research and product development. You will architect, build, and run platforms spanning AWS and on‑premises, enabling teams to train and deploy complex models at scale.

You’ll collaborate with AI researchers and engineers to design tools, automate deployment cycles, and optimize performance and cost across cloud and bare‑metal environments.