Principal Software Engineering, AI Infrastructure

Develop systems for AI Workload management, scheduling, preemption, and failover. Develop systems for AI Workload serving. Design and build GPU accelerated infrastructure for training and inference workloads, spanning bare metal, virtual machines, and containerized environments. Build and operate advanced orchestration and resource governance scenarios using platforms such as AKS and related Kubernetes ecosystem capabilities to enable fair sharing, isolation, and efficient utilization of accelerated resources. Design, Develop, test, and maintain services written in C#, Golang hosted on Kubernetes clusters. Optimize performance, reliability, and utilization across large GPU fleets, including scaleup and scale out configurations. Drive end-to-end platform features from design through production, including observability, diagnostics, and operational excellence. Influence platform architecture and technical direction across teams through design reviews and technical leadership. Embody our Culture and Values Bachelor's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, Linux, Go or Python Master's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, Linux, Go or Python OR Bachelor's Degree in Computer Science or related technical field AND 12+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, linux, Go or Python OR equivalent experience. Proven ability to design and operate large scale, production infrastructure with high reliability and performance requirements. Demonstrated problem-solving skills and the ability to debug complex, cross layer systems issues. Demonstrated technical leadership, including mentoring engineers and driving cross team architectural alignment. Hands-on experience with resource scheduling, preemption, priority handling on large clusters. Advanced collaboration and communication skills, with the ability to work across organizational boundaries.

with large-scale online Inferencing services.

in building or operating multitenant AI platforms in cloud environments.

Familiarity with inference and/or distributed training stacks (e.g., NCCL style collectives, model/data parallelism, Large-Language Models).

Familiarity with GPU accelerated computing (e.g., CUDA, GPU drivers, device plugins, or runtime integration).

Dev-ops experience with microservices architecture in a complex infrastructure and operational environment.