Systems Architect AI/ML Infrastructure

Deepgram's infrastructure spans bare metal GPU clusters, multi-cloud deployments, and global edge presence—all serving real-time voice AI at massive scale while simultaneously powering large-scale model training. As a Systems Architect, you will own the end-to-end infrastructure architecture that makes this possible. You will design the compute, storage, and networking systems that serve both production inference and research training workloads, build multi-cloud strategies that balance performance with cost, and create burstable infrastructure that scales with Deepgram's rapidly growing demands. This is a senior technical leadership role where your architectural decisions shape the foundation everything at Deepgram runs on.

Responsibilities

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

AI-first mindset—AI use and comfort aren’t optional, they’re core to how we operate, innovate, and measure performance; 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; 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 have

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