Site Reliability Engineer - AI & ML Infrastructure (Kubernetes, AWS & Terraform)

Deepgram

32d

About The Position

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+ organizations build voice offerings that are ‘Powered by Deepgram’, including Twilio, Cloudflare, Sierra, Decagon, Vapi, Daily, Cresta, Granola, and Jack in the Box. Deepgram’s voice-native foundation models are accessed through cloud APIs or as self-hosted and on-premises software, with unmatched accuracy, low latency, and cost efficiency. Backed by a recent Series C led by leading global investors and strategic partners, Deepgram has processed over 50,000 years of audio and transcribed more than 1 trillion words. There is no organization in the world that understands voice better than Deepgram. 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 even build your own into your everyday work. We measure how effectively AI is applied to deliver results, and consistent, creative use of the latest AI capabilities is key to success here. Candidates should be comfortable adopting new models and modes quickly, integrating AI into their workflows, and continuously pushing the boundaries of what these technologies can do. Additionally, we move at the pace of AI. Change is rapid, and you can expect your day-to-day work to evolve just as quickly. This may not be the right role if you’re not excited to experiment, adapt, think on your feet, and learn constantly, or if you’re seeking something highly prescriptive with a traditional 9-to-5. We're looking for an experienced Site Reliability Engineer to build and operate the hybrid infrastructure foundation for our advanced AI/ML research and product development. You'll architect, build, and run the platform spanning AWS and our bare metal data centers, empowering our teams to train and deploy complex models at scale. This role is focused on creating a robust, self-service environment using Kubernetes, AWS, and Infrastructure-as-Code (Terraform), and orchestrating high-demand GPU workloads using schedulers like Slurm.

Requirements

5+ years of experience in Platform Engineering, DevOps, or Site Reliability Engineering (SRE).
Proven, hands-on experience building and managing production infrastructure with Terraform.
Expert-level knowledge of Kubernetes architecture and operations in a large-scale environment.
Experience with high-performance compute (HPC) job schedulers, specifically Slurm, for managing GPU-intensive AI workloads.
Experience managing bare metal infrastructure, including server provisioning (e.g., PXE boot, MAAS), configuration, and lifecycle management.
Strong scripting and automation skills (e.g., Python, Go, Bash).

Nice To Haves

Experience with CI/CD systems (e.g., GitLab CI, Jenkins, ArgoCD) and building developer tooling.
Familiarity with FinOps principles and cloud cost optimization strategies.
Knowledge of Kubernetes networking (e.g., Calico, Cilium) and storage (e.g., Ceph, Rook) solutions.
Experience in a multi-region or hybrid cloud environment.

Responsibilities

Architect and maintain our core computing platform using Kubernetes on AWS and on-premise, providing a stable, scalable environment for all applications and services.
Develop and manage our entire infrastructure using Infrastructure-as-Code (IaC) principles with Terraform, ensuring our environments are reproducible, versioned, and automated.
Design, build, and optimize our AI/ML job scheduling and orchestration systems, integrating Slurm with our Kubernetes clusters to efficiently manage GPU resources.
Provision, manage, and maintain our on-premise bare metal server infrastructure for high-performance GPU computing.
Implement and manage the platform's networking (CNI, service mesh) and storage (CSI, S3) solutions to support high-throughput, low-latency workloads across hybrid environments.
Develop a comprehensive observability stack (monitoring, logging, tracing) to ensure platform health, and create automation for operational tasks, incident response, and performance tuning.
Collaborate with AI researchers and ML engineers to understand their infrastructure needs and build the tools and workflows that accelerate their development cycle.
Automate the life cycle of single-tenant, managed deployments