Job Description

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

• 서류ì „í˜• - 온라인 코딩테스트 - í™”ìƒë©´ì ‘ (1시간 내외) - ëŒ€ë©´ë©´ì ‘ (3시간 내외) - 최종합격

• ì „í˜•ì ˆì°¨ëŠ” 직무별로 다르게 ìš´ì˜ë ìˆ˜ 있으며, ì¼ì • 및 상황에 따라 ë³€ë™ë ìˆ˜ 있습니다.

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Additional Information

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