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Perplexity Logo Perplexity · IT - Software Development

AI Infra Engineer

United States, San Francisco 210000.00 - 385000.00 USD / Year · Job Posted February 21, 2026
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Job Description

We are looking for an AI Infra engineer to join our growing team. We work with Kubernetes, Slurm, Python, C++, PyTorch, and primarily on AWS. As an AI Infrastructure Engineer, you will be partnering closely with our Inference and Research teams to build, deploy, and optimize our large-scale AI training and inference clusters

Job Responsibility

  • 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 both training pipelines and inference services
  • Implement resource scheduling and job management systems across heterogeneous compute environments
  • Benchmark system performance, diagnose bottlenecks, and implement improvements across both training and inference infrastructure
  • Build monitoring, alerting, and observability solutions tailored to ML workloads running on Kubernetes and Slurm
  • Respond swiftly to system outages and collaborate across teams to maintain high uptime for critical training runs and inference services
  • Optimize cluster utilization and implement autoscaling strategies for dynamic workload demands

Requirements

  • Strong expertise in Kubernetes administration, including custom resource definitions, operators, and cluster management
  • Hands-on experience with Slurm workload management, including job scheduling, resource allocation, and cluster optimization
  • Experience with 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 processes (Multi-Head Attention, Multi/Grouped-Query, distributed training strategies)
  • Experience managing GPU clusters and optimizing compute resource utilization
  • Expert-level Kubernetes administration and YAML configuration management
  • Proficiency with Slurm job scheduling, resource management, and cluster configuration
  • Python and C++ programming with focus on systems and infrastructure automation
  • Hands-on experience with ML frameworks such as 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 both batch and real-time workloads
  • Solid debugging and monitoring skills with expertise in observability tools for containerized environments
  • Demonstrated experience managing large-scale Kubernetes deployments in production environments
  • Proven track record with Slurm cluster administration and HPC workload management
  • Previous roles in SRE, DevOps, or Platform Engineering with focus on ML infrastructure
  • Experience supporting both long-running training jobs and high-availability inference services
  • Ideally, 3-5 years of relevant experience in ML systems deployment with specific focus on cluster orchestration and resource management

Nice to have

  • Experience with Kubernetes operators and custom controllers for ML workloads
  • Advanced Slurm administration including multi-cluster federation and advanced scheduling policies
  • Familiarity with GPU cluster management and CUDA optimization
  • Experience with other ML frameworks like TensorFlow or distributed training libraries
  • Background in HPC environments, parallel computing, and high-performance networking
  • Knowledge of infrastructure as code (Terraform, Ansible) and GitOps practices
  • Experience with container registries, image optimization, and multi-stage builds for ML workloads

What we offer

  • Equity
  • Health
  • Dental
  • Vision
  • Retirement
  • Fitness
  • Commuter and dependent care accounts
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