P
Perplexityvia Ashby
Member of Technical Staff (AI Infrastructure Engineer)
San Francisco$220K - $405K/yrPosted 1mo ago
MLOpsStaff+Full-time#ai-lab
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About the Role
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
- 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 specific focus on cluster orchestration and resource management
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
- 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 specific focus on cluster orchestration and resource management
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