Member of Technical Staff (AI Infrastructure Engineer)

Stack

Responsibilities

• ▹Design, deploy, and maintain scalable Kubernetes clusters for AI model inference and training workloads
• ▹Manage and optimize Slurm-based HPC environments for distributed training of large language models
• ▹Develop robust APIs and orchestration systems for training pipelines and inference services
• ▹Implement resource scheduling and job management across heterogeneous compute environments
• ▹Benchmark system performance, diagnose bottlenecks, and improve both training and inference infrastructure
• ▹Build monitoring, alerting, and observability solutions for ML workloads on Kubernetes and Slurm
• ▹Respond quickly to outages and collaborate across teams to maintain high uptime for training and inference
• ▹Optimize cluster utilization and implement autoscaling strategies for dynamic workload demands

Requirements

• ▹Strong Kubernetes administration expertise (CRDs, operators, cluster management)
• ▹Hands-on Slurm workload management (job scheduling, resource allocation, cluster optimization)
• ▹Experience deploying and managing distributed training systems at scale
• ▹Deep understanding of container orchestration and distributed systems architecture
• ▹High-level familiarity with LLM architecture and training (Multi-Head/Multi-Query Attention, distributed training strategies)
• ▹Experience managing GPU clusters and optimizing compute utilization
• ▹Expert-level Kubernetes administration and YAML configuration management
• ▹Python and C++ programming with a systems/infrastructure automation focus
• ▹Hands-on experience with ML frameworks (PyTorch) in distributed training contexts
• ▹Strong understanding of networking, storage, and compute resource management for ML workloads
• ▹Experience developing APIs and managing distributed systems for batch and real-time workloads
• ▹Solid debugging and monitoring skills for containerized environments

Nice to have

• ▹Kubernetes operators and custom controllers for ML workloads
• ▹Advanced Slurm administration including multi-cluster federation and scheduling policies
• ▹GPU cluster management and CUDA optimization
• ▹Familiarity with other ML frameworks (TensorFlow) or distributed training libraries
• ▹HPC, parallel computing, and high-performance networking background
• ▹Infrastructure as code (Terraform, Ansible) and GitOps practices
• ▹Container registries, image optimization, and multi-stage builds for ML workloads

Soft skills