Job Overview
Location
USA | Remote
Job Type
Full-time
Category
Software Engineering
Date Posted
July 4, 2026
Full Job Description
📋 Description
- • Architect, build, and maintain a hybrid infrastructure platform spanning AWS cloud and on-premise bare metal data centers to support AI/ML research and production workloads.
- • Design and manage infrastructure using Infrastructure-as-Code (IaC) with Terraform to ensure environments are reproducible, version-controlled, and fully automated.
- • Deploy and operate Kubernetes clusters across both cloud and on-premise environments to provide a stable, scalable, and self-service platform for AI/ML teams.
- • Integrate and optimize Slurm job scheduler with Kubernetes to efficiently manage high-demand GPU workloads for training and inference of voice AI models.
- • Provision, configure, and lifecycle-manage bare metal servers using tools like PXE boot and MAAS for high-performance computing requirements.
- • Implement and maintain networking solutions including CNI plugins and service mesh to support low-latency, high-throughput communication across hybrid environments.
- • Design and manage storage solutions using CSI drivers and S3-compatible systems to handle massive audio data ingestion and model artifact storage.
- • Build and operate a comprehensive observability stack including monitoring, logging, and tracing to ensure platform reliability, performance, and rapid incident response.
- • Automate operational tasks, incident remediation, and performance tuning to reduce toil and improve platform resilience.
- • Collaborate directly with AI researchers and ML engineers to understand their infrastructure needs and co-build tools that accelerate model development and deployment cycles.
- • Automate the provisioning and lifecycle management of single-tenant, managed deployments for enterprise customers using self-service workflows.
- • Maintain a strong AI-first mindset by actively using and integrating advanced AI tools into daily workflows to improve efficiency and innovation.
- • Adapt rapidly to evolving AI technologies and infrastructure demands, embracing change and continuous learning as core to the role.
- • Treat infrastructure as a product, continuously improving the developer experience through automation, documentation, and user-centric design.
- • Ensure cost efficiency and performance optimization across hybrid cloud and on-premise environments using FinOps principles and resource scheduling strategies.
🎯 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)
🏖️ Benefits
- • Medical, dental, vision benefits
- • Annual wellness stipend
- • Mental health support
- • Life, STD, LTD Income Insurance Plans
- • Unlimited PTO
- • Parental leave
- • Flexible schedule
- • 12 Paid US company holidays
- • Quarterly personal productivity stipend
- • One-time stipend for home office upgrades
- • 401(k) plan with company match
- • Tax Savings Programs
- • Learning / Education stipend
- • Participation in talks and conferences
- • Employee Resource Groups
- • AI enablement workshops / sessions
Skills & Technologies
See exactly how your profile matches this role — strengths, skill gaps, and what to do about them.
About Deepgram Inc.
Deepgram builds end-to-end speech AI infrastructure that converts live or recorded audio into text and insights. The company trains large-scale neural networks on GPU clusters to deliver low-latency transcription, keyword detection, and speaker diarization through a single API. Developers use the platform for call centers, meetings, podcasts, and voice bots, paying per minute or hosting the engine on-premise. Founded in 2015 and headquartered in San Francisco, Deepgram serves enterprises seeking accurate, private, and customizable speech recognition without vendor lock-in.
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