Deepgram
Systems Architect AI/ML Infrastructure
Vaga remota de Engineering com fit claro de localização do candidato.
Publicada6 de abr. de 2026
Países elegíveis1 país aceito
Sinal de senioridadeLead
Modelo de trabalhoRemoto
Locais aceitos para candidatos
Estados Unidos
Resumo da vaga
Systems Architect AI/ML Infrastructure
Requisitos e responsabilidades
Conteúdo da vaga extraído em seções para revisão mais rápida.
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
You'll Love This Role If You
- Think in systems -- you naturally see the connections between compute, storage, network, and how they interact under load
- Are motivated by designing infrastructure that operates at the intersection of real-time production systems and large-scale ML training
- Enjoy making architectural trade-offs where cost, performance, reliability, and velocity are all in tension
- Want to work across the full infrastructure stack -- from bare metal and GPUs to cloud services and container orchestration
- Are excited about building cost-effective, burstable infrastructure that enables world-class AI research
- Like operating at a strategic level while staying technically deep enough to validate designs and debug complex issues
It's Important To Us That You Have
- 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 (see our Network Engineer role for the deep specialist)
It Would Be Great If You Had
- 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
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