Principal Software Engineering, AI Infrastructure
Core
Design and build GPU-accelerated infrastructure for AI training and inference workloads, including workload management, scheduling, and serving across bare metal, VMs, and containers.
Role type
Principal Software Engineering, AI Infrastructure
Builds
GPU-accelerated infrastructure for training and inference, orchestration, and resource governance platforms
Domain
Cloud Infrastructure, AI/ML Systems, GPU Computing
Deliverable
production ML models
Required skills
C#, Golang, Kubernetes, GPU accelerated computing, resource scheduling, system design, observability, distributed training stacks, microservices architecture
Preferred skills
CUDA, NCCL, Large-Language Models, multitenant AI platforms, cross-team architectural alignment
Technologies
AKS, Kubernetes, C#, Golang, Linux, CUDA
Responsibilities
Develop systems for AI workload management, scheduling, preemption, and failover; Design and build GPU-accelerated infrastructure for training and inference; Build and operate advanced orchestration and resource governance scenarios; Design, develop, test, and maintain services on Kubernetes clusters; Optimize performance and utilization across large GPU fleets; Drive end-to-end platform features from design through production; Influence platform architecture and technical direction across teams.
Seniority
Principal, hands-on IC with technical leadership
