Perplexity14.04.2026
Member of Technical Staff (AI Infrastructure Engineer)
Полная занятостьУдалёнка
Обязанности
- 01Design, deploy, and maintain scalable Kubernetes clusters for AI model inference and training workloads
- 02Manage and optimize Slurm-based HPC environments for distributed training of large language models
- 03Develop robust APIs and orchestration systems for both training pipelines and inference services
- 04Implement resource scheduling and job management systems across heterogeneous compute environments
- 05Benchmark system performance, diagnose bottlenecks, and implement improvements across both training and inference infrastructure
- 06Build monitoring, alerting, and observability solutions tailored to ML workloads running on Kubernetes and Slurm
- 07Respond swiftly to system outages and collaborate across teams to maintain high uptime for critical training runs and inference services
- 08Optimize cluster utilization and implement autoscaling strategies for dynamic workload demands
Требования
- 01Strong expertise in Kubernetes administration, including custom resource definitions, operators, and cluster management
- 02Hands-on experience with Slurm workload management, including job scheduling, resource allocation, and cluster optimization
- 03Experience with deploying and managing distributed training systems at scale
- 04Deep understanding of container orchestration and distributed systems architecture
- 05High level familiarity with LLM architecture and training processes (Multi-Head Attention, Multi/Grouped-Query, distributed training strategies)
- 06Experience managing GPU clusters and optimizing compute resource utilization
- 07Expert-level Kubernetes administration and YAML configuration management
- 08Proficiency with Slurm job scheduling, resource management, and cluster configuration
- 09Python and C++ programming with focus on systems and infrastructure automation
- 10Hands-on experience with ML frameworks such as PyTorch in distributed training contexts
- 11Strong understanding of networking, storage, and compute resource management for ML workloads
- 12Experience developing APIs and managing distributed systems for both batch and real-time workloads
- 13Solid debugging and monitoring skills with expertise in observability tools for containerized environments
- 14Demonstrated experience managing large-scale Kubernetes deployments in production environments
- 15Proven track record with Slurm cluster administration and HPC workload management
- 16Previous roles in SRE, DevOps, or Platform Engineering with focus on ML infrastructure
- 17Experience supporting both long-running training jobs and high-availability inference services
- 18Ideally, 3-5 years of relevant experience in ML systems deployment with specific focus on cluster orchestration and resource management