AI Infrastructure Engineer

501-1000

South Korea

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Location

Pangyo, South Korea

Salary

(Yearly)

(Hourly)

Undisclosed

We are looking for the best

At 42dot, our AI Infrastructure Engineer manages the high-performance AI infrastructure orchestrating thousands of GPUs across multiple data centers. You will contribute to the scaling, monitoring, and operational optimization required to maintain a robust and world-class computing environment.

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.

Qualifications

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.

Preferred Qualifications

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.

Interview Process

서류전형 - 온라인 코딩테스트 - 화상면접 (1시간 내외) - 대면면접 (3시간 내외) - 최종합격
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전형일정 및 결과는 지원서에 등록하신 이메일로 개별 안내드립니다.

Additional Information

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