Software Engineer (Seed Model - Infrastructures - SRE)

What you'll do

• Maintain machine-learning systems supporting large-model development, training, and deployment; troubleshoot production issues.
• Plan GPU and related compute, storage, and network resources, including cost and budget, and provide resource data for decisions.
• Improve cluster and service stability, utilization, and operational efficiency; manage disaster recovery, deployments, and machine lifecycles across regions and data centers.

What they ask for

• 1+ years of experience in DevOps, SRE, or infrastructure engineering projects.
• Proficient in at least one or two of Go / Python / Shell in a Linux environment.
• Experience operating distributed resource-management and job-scheduling systems; familiar with Kubernetes ecosystem and architecture.
• Familiar with containerization technologies such as Docker / Kata.

Nice to have

• Hands-on experience operating GPU clusters or ML training / inference platforms.
• Familiarity with observability tooling and on-call practices; contributions to automation or platform tooling that improved utilization or efficiency.

Tools

Go, Python, Shell, Linux, Kubernetes, Docker, Kata

AI focus

Supports the infrastructure for training and inference of AI foundation models, including large-model development and deployment.