[Hiring] Systems Architect AI/ML Infrastructure @Deepgram

Role Description

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:

• 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.

Qualifications

• 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.

Requirements

• 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.

Benefits

• Holistic health: Medical, dental, vision benefits.
• Annual wellness stipend.
• Mental health support.
• Life, STD, LTD Income Insurance Plans.
• Unlimited PTO.
• Generous paid 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.