staff software engineering Staff Engineer tech_leadership · Posted Apr 13, 2026
$220,000 – $405,000
USD per year

About this role

Perplexity is hiring a staff-level Staff Engineer in the software engineering function based in San Francisco, CA. The posting calls out experience with CUDA, Kubernetes, Terraform, Ansible. Compensation is listed at $220,000–$405,000 per year.

Role
Staff Engineer
Function
software engineering
Level
staff
Track
Tech leadership
Employment
Full-time
Location
San Francisco, CA
Department
AI
Posted
Apr 13, 2026
AI Summary
Design and maintain scalable Kubernetes and Slurm clusters for AI training and inference at scale. Build APIs, orchestration systems, and monitoring solutions for large language model workloads. Requires expert Kubernetes administration, Slurm expertise, Python/C++ programming, and deep distributed systems knowledge.

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Job description

from Perplexity careers

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

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 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

Qualifications

  • 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

    This is an excerpt. Read the full job description on Perplexity careers →
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