Architect, deploy, and manage scalable Kubernetes environments tailored for AI training and inference workloads
Operate and fine-tune Slurm-managed high-performance computing clusters for distributed large language model training
Create reliable APIs and workflow orchestration systems supporting training and inference pipelines
Develop resource allocation and job scheduling frameworks across diverse computing platforms
Evaluate system performance, identify performance constraints, and apply targeted optimizations for training and inference systems
Design monitoring, alerting, and observability tooling specific to machine learning operations on Kubernetes and Slurm
Troubleshoot infrastructure incidents rapidly and coordinate with cross-functional teams to ensure continuous operation of critical systems
Improve cluster efficiency through intelligent autoscaling and resource utilization strategies for fluctuating workloads

Competitive salary and equity package

Hybrid

Collaborative engineering team focused on AI systems infrastructure

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

Available for qualified candidates

Perplexity is hiring a Member of Technical Staff (AI Infrastructure Engineer)