Senior/Staff Platform Engineer

Salary: $160K - $220K Employment Type: Full-Time Location Type: Remote

Company Overview

Deepgram is the leading voice AI platform for developers building speech-to-text (STT), text-to-speech (TTS), and full speech-to-speech (STS) offerings. Over 200,000 developers build with Deepgram’s voice-native foundational models – accessed through APIs or as self-managed software – due to our unmatched accuracy, latency, and pricing. Customers include software companies building voice products, co-sell partners working with large enterprises, and enterprises solving internal voice AI use cases. The company ended 2024 cash-flow positive with 400+ enterprise customers, 3.3x annual usage growth across the past 4 years, over 50,000 years of audio processed, and over 1 trillion words transcribed. There is no organization in the world that understands voice better than Deepgram.

Opportunity

We're looking for an expert (Senior/Staff-level) Platform 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.

What You’ll Do

• 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.

You’ll Love This Role If You

• Are passionate about building platforms that empower developers and researchers.
• Enjoy creating elegant, automated solutions for complex infrastructure challenges in both cloud and data center environments.
• Thrive on optimizing hybrid infrastructure for performance, cost, and reliability.
• Are excited to work at the intersection of modern platform engineering and cutting-edge AI.
• Love to treat infrastructure as a product, continuously improving the developer experience.

It’s Important To Us That You Have

• 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).

It Would Be Great If You Had

• 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 (CNI) and service mesh technologies.