AI Infrastructure Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 1 hour agoStatus: Live

Job Summary

Operate and maintain a large-scale GPU cluster spanning multiple data centers, using Kubernetes and Slurm to orchestrate thousands of GPUs. Monitor and troubleshoot GPU hardware and software issues to ensure high availability and fast recovery. Build automation tools with Python or Shell, manage GPU quota for ML teams, and support the architecture and performance tuning of distributed training environments for large-scale autonomous driving models.

Location: South Korea

Workplace: Hybrid

Employment Type: Full time

Job Function: DevOps, Cloud & Infrastructure

Key Responsibilities

•Operate and maintain a large-scale GPU cluster across multiple data centers using Kubernetes and Slurm.
•Monitor and diagnose failures across GPU hardware and software stacks to ensure high availability and rapid recovery.
•Develop automation tools and scripts in Python or Shell to streamline repetitive infrastructure management.
•Manage GPU resource quotas and provide technical support to ML researchers for optimal computing utilization.
•Participate in architectural design and performance tuning for distributed training environments for large-scale autonomous driving models.

Key Requirements

•Strong understanding of Linux, including kernel operations, process management, and system security.
•Practical experience with containerization and orchestration using Docker and Kubernetes.
•Understanding of networking fundamentals (TCP/IP, HTTP(S)) with basic troubleshooting ability.
•Ability to write maintainable automation/system administration scripts in Python or Shell.
•Logical problem-solving skills to identify and resolve root causes in complex, large-scale systems.

Experience:AI infrastructureGPU clustersDistributed trainingAutonomous driving modelsDeep learning

Skills:CommunicationProblem-solving

Tech Stack:LinuxKubernetesSlurmGPUDockerPythonShellTCP/IPHTTP(S)PrometheusGrafanaDatadogAWSGCPCUDANCCLPyTorchTensorFlowTerraform

Company Brief

42dot

Website

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42dot

1 week ago

AI Infrastructure Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 1 hour agoStatus: Live

Job Summary

Location: South Korea

Workplace: Hybrid

Employment Type: Full time

Job Function: DevOps, Cloud & Infrastructure

Key Responsibilities

•Operate and maintain a large-scale GPU cluster across multiple data centers using Kubernetes and Slurm.
•Monitor and diagnose failures across GPU hardware and software stacks to ensure high availability and rapid recovery.
•Develop automation tools and scripts in Python or Shell to streamline repetitive infrastructure management.
•Manage GPU resource quotas and provide technical support to ML researchers for optimal computing utilization.
•Participate in architectural design and performance tuning for distributed training environments for large-scale autonomous driving models.

Key Requirements

•Strong understanding of Linux, including kernel operations, process management, and system security.
•Practical experience with containerization and orchestration using Docker and Kubernetes.
•Understanding of networking fundamentals (TCP/IP, HTTP(S)) with basic troubleshooting ability.
•Ability to write maintainable automation/system administration scripts in Python or Shell.
•Logical problem-solving skills to identify and resolve root causes in complex, large-scale systems.

Experience:AI infrastructureGPU clustersDistributed trainingAutonomous driving modelsDeep learning

Skills:CommunicationProblem-solving

Tech Stack:LinuxKubernetesSlurmGPUDockerPythonShellTCP/IPHTTP(S)PrometheusGrafanaDatadogAWSGCPCUDANCCLPyTorchTensorFlowTerraform

Company Brief

42dot

Website