AI Infra Engineer

Details

Perplexity is an AI-powered answer engine founded in December 2022 and growing rapidly as one of the world’s leading AI platforms. Perplexity has raised over $1B in venture investment from some of the world’s most visionary and successful leaders. Our objective is to build accurate, trustworthy AI that powers decision-making for people and assistive AI wherever decisions are being made. Curious people use Perplexity to answer more than 780 million queries every month.

We are looking for an AI Infra engineer to join our growing team. You will partner closely with our Inference and Research teams to build, deploy, and optimize large-scale AI training and inference clusters. Work is primarily on AWS and uses Kubernetes, Slurm, Python, C++, and PyTorch.

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 systems across heterogeneous compute environments
• Benchmark system performance, diagnose bottlenecks, and implement improvements across 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: job scheduling, resource allocation, and 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 processes (Multi-Head Attention, Multi/Grouped-Query, distributed training strategies)
• Experience managing GPU clusters and optimizing compute resource utilization

Required Skills

• 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

Preferred Skills

• 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

Required Experience

• 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 focus on cluster orchestration and resource management

Compensation & Benefits

• Cash compensation range: $190,000 - $250,000 per year
• Equity may be part of the total compensation package
• Comprehensive health, dental, and vision insurance for you and your dependents
• 401(k) plan