System Engineer

The company has almost 100 million customers based in Japan and 1 billion globally as well, providing more than 70 services in a variety such as e-commerce, payment services, financial services, telecommunication, media, sports, etc. GPUOD team. Optimize Kubernetes (K8s) for GPU workloads, including scheduling policies, autoscaling, and multi-tenant resource isolation. Deploy and maintain inference serving platforms (e.g., NVIDIA Triton, vLLM, SGlang) for high-throughput and low-latency model deployment. Automate cluster provisioning, monitoring, and recovery to maximize uptime and GPU utilization. Collaborate with ML engineers to troubleshoot GPU-related issues in training jobs (e.g., NCCL errors, OOM) and inference bottlenecks. Implement observability tools (Prometheus, Grafana) to track GPU utilization, job performance, and cluster health. Develop infrastructure-as-code (IaC) solutions for reproducible GPU environments (e.g., Terraform, Ansible).