Base Salary
$160k - $220k/yr
Responsibilities
- Define and drive end-to-end infrastructure architecture for production inference and research training AI/ML workloads.
- Design multi-cloud and hybrid infrastructure strategies balancing performance, reliability, cost, and vendor flexibility.
- Architect compute orchestration systems for GPU and CPU workloads across heterogeneous infrastructure.
- Design storage architectures for high-throughput training data pipelines and low-latency model serving.
- Lead capacity planning and growth modeling across infrastructure dimensions.
- Drive infrastructure cost optimization and FinOps practices.
- Design burstable, elastic training infrastructure that scales with demand while minimizing idle cost.
- Architect research compute infrastructure for ML teams while maintaining operational efficiency.
- Establish architectural standards, design review processes, and technical documentation practices.
- Collaborate with engineering leadership to align infrastructure strategy with product and business objectives.
- Evaluate emerging hardware, cloud services, and infrastructure technologies.
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 block, object, and file storage, including performance tuning for large-scale data workloads.
- Strong experience with compute orchestration using Kubernetes and workload scheduling across heterogeneous infrastructure.
- Hands-on experience with GPU infrastructure, including procurement considerations, cluster design, driver management, and runtime management.
- Track record of capacity planning and infrastructure scaling for high-growth environments.
- Ability to communicate complex architectural decisions clearly to technical and non-technical stakeholders.
- Strong understanding of networking fundamentals relevant to infrastructure architecture.
- Preferred: experience architecting ML training infrastructure, including distributed training, large dataset management, and experiment infrastructure.
- Preferred: background in cost optimization and FinOps for large-scale cloud and bare metal infrastructure.
- Preferred: experience operating bare metal infrastructure in colocation facilities.
- Preferred: expertise in network architecture, high-bandwidth GPU interconnects, and global traffic routing.
- Preferred: experience with infrastructure modeling and simulation for capacity planning.
- Preferred: familiarity with Slurm, Ray, or other HPC/ML job scheduling systems.
- Preferred: understanding of power, cooling, and physical infrastructure considerations for GPU-dense deployments.
Benefits
- Medical, dental, and vision benefits; annual wellness stipend; mental health support; life, STD, and LTD income insurance plans.
- Unlimited PTO, generous paid parental leave, flexible schedule, and 12 paid US company holidays.
- Quarterly personal productivity stipend, one-time home office upgrade stipend, 401(k) with company match, and tax savings programs.
- Learning and education stipend, participation in talks and conferences, employee resource groups, and AI enablement workshops.
- Benefits for international employees are administered locally through an Employer of Record and vary by region.
Tech Stack
Categories
About Deepgram
Deepgram is the real-time API platform powering the trillion-dollar Voice AI economy. Backed by a $130M Series C at a $1.3B valuation, Deepgram is trusted by 200,000+ developers and 1,300+ organizations to build Voice AI products, platforms, and autonomous agents with the lowest latency, highest accuracy, and enterprise reliability. Our voice-native foundation models and runtime infrastructure have processed 50,000+ years of audio and over 1 trillion words, making Deepgram the most experienced voice AI platform in the world. Industry-leading models & platform: 👂 Nova-3 — the world’s most accurate real-time speech-to-text model 🔊 Aura-2 — professional, enterprise-grade text-to-speech 💬 Flux — the first Conversational Speech Recognition model designed to handle interruptions 🚀 Voice Agent API — enterprise-ready, real-time conversational AI 🧠 Saga — the Voice OS Beyond core infrastructure, Deepgram is expanding the Voice AI ecosystem through: 💪 Powered by Deepgram, supporting voice products built by leading AI startups and enterprise organizations 🌉 A new Voice AI Collaboration Hub in San Francisco for builders, partners, and the voice community 🍔 The acquisition of OfOne, delivering real-time Voice AI for restaurants and drive-thru operations with 95%+ containment 📃 A growing patent portfolio in Voice AI Much like APIs powered the payments and cloud economies, Deepgram is building the foundation for a trillion-dollar B2B Voice AI economy—centered on the most natural human interface: voice.