42dot

AI Infrastructure Engineer

42dot

full-time

Posted on:

Location Type: Hybrid

Location: Pangyo • South Korea

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Tech Stack

AWSCloudDockerGoogle Cloud PlatformGrafanaKubernetesLinuxPrometheusPythonPyTorchTCP/IPTensorflowTerraform

About the role

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

Requirements

  • Strong proficiency in Linux operating systems, including a solid understanding of kernel operations, process management, and system security.
  • Practical experience with containerization technologies (Docker) and orchestration (Kubernetes), including building, managing, and troubleshooting containerized environments.
  • Solid understanding of networking fundamentals, including TCP/IP and HTTP(S), with the ability to perform basic network troubleshooting.
  • Ability to write clean and maintainable scripts in Python or Shell for automation and system administration.
  • Logical approach to problem-solving with the persistence to identify and resolve root causes in complex, large-scale systems.
  • Strong communication skills to effectively collaborate with cross-functional teams and external partners.
  • Experience in building observability stacks with Prometheus, Grafana, and Datadog for large-scale clusters.
  • Experience in building or operating infrastructure on public cloud platforms such as AWS or GCP.
  • Knowledge of the NVIDIA accelerated computing stack, including drivers, CUDA, and NCCL.
  • Familiarity with the ML model training lifecycle and deep learning frameworks such as PyTorch or TensorFlow.
  • Experience with large-scale workload managers or resource scheduling tools such as Kubernetes or Slurm.
  • Familiarity with Infrastructure as Code (IaC) tools such as Terraform to manage complex infrastructure.
Benefits
  • 이력서 제출 시 주민등록번호, 가족관계, 혼인 여부, 연봉, 사진, 신체조건, 출신 지역 등 채용절차법상 요구 금지된 정보는 제외 부탁드립니다.
  • 모든 제출 파일은 30MB 이하의 PDF 양식으로 업로드를 부탁드립니다. (이력서 업로드 중 문제가 발생한다면 지원하시고자 하는 포지션의 URL과 함께 이력서를 recruit@42dot.ai으로 전송 부탁드립니다.)
  • 인터뷰 프로세스 종료 후 지원자의 동의하에 평판조회가 진행될 수 있습니다.
  • 국가보훈대상자 및 취업보호 대상자는 관계법령에 따라 우대합니다.
  • 장애인 고용 촉진 및 직업재활법에 따라 장애인 등록증 소지자를 우대합니다.
  • 42dot은 의뢰하지 않은 서치펌의 이력서를 받지 않으며, 요청하지 않은 이력서에 대해 수수료를 지불하지 않습니다.
Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools
PythonShellLinuxDockerKubernetesTCP/IPHTTP(S)PrometheusGrafanaAWS
Soft Skills
problem-solvingcommunicationcollaboration