Systems Architect AI/ML Infrastructure

Designs end-to-end infrastructure architecture for AI/ML workloads, including multi-cloud strategies, GPU orchestration, storage for massive datasets, and capacity planning to support production inference and research training at scale. Requires 7+ years in systems architecture with deep expertise in Kubernetes, AWS, and GPU infrastructure.

About the job

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