Systems Architect AI/ML Infrastructure

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 flexibility
• Architect compute orchestration systems that efficiently schedule and manage GPU and CPU workloads across heterogeneous infrastructure
• Design storage architectures that handle the massive datasets required for speech and audio ML -- from high-throughput training data pipelines to low-latency model serving
• Lead capacity planning across all infrastructure dimensions, modeling growth and ensuring Deepgram can scale ahead of demand
• Drive cost optimization and FinOps practices, identifying opportunities to reduce infrastructure spend without compromising performance or reliability
• Design burstable, elastic training infrastructure that can scale up for large training runs and scale down to minimize idle cost
• Architect research compute infrastructure that gives ML teams the resources they need while maintaining operational efficiency
• Establish architectural standards, design review processes, and technical documentation practices for infrastructure decisions
• Collaborate with engineering leadership to align infrastructure strategy with product roadmap and business objectives
• Evaluate emerging hardware, cloud services, and infrastructure technologies for potential adoption

Requirements

• 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 on-premises environment
• Deep expertise in storage system design -- block, object, and file storage, including performance tuning for large-scale data workloads
• Strong experience with compute orchestration using Kubernetes, and an understanding of how to schedule diverse workloads efficiently
• Hands-on experience with GPU infrastructure -- procurement considerations, cluster design, driver and runtime management
• Track record of capacity planning and infrastructure scaling for high-growth environments
• Ability to communicate complex architectural decisions clearly to both technical and non-technical stakeholders
• Strong understanding of networking fundamentals as they relate to infrastructure architecture

Nice-to-Haves

• 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
• Experience operating and managing bare metal infrastructure in colocation facilities
• Expertise in network architecture design, including high-bandwidth GPU interconnects and global traffic routing
• Experience with infrastructure modeling and simulation for capacity planning
• Familiarity with Slurm, Ray, or other HPC/ML job scheduling systems
• Understanding of power, cooling, and physical infrastructure considerations for GPU-dense deployments