Software Engineer (Seed Model - Infrastructures - SRE)

What you'll do

• Maintain ML systems supporting large-model development, training, and deployment; troubleshoot production and system issues.
• Plan and manage GPU, CPU, storage, and related resources, including costs and budgets, and provide resource data for decisions.
• Improve cluster and service stability, resource 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.
• Proficiency in at least one or two of Go / Python / Shell in a Linux environment.
• Experience operating distributed-system resource management and job scheduling systems; familiarity with the Kubernetes ecosystem and architecture.
• Familiarity 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 resource utilization or operational efficiency.

Tools

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

AI focus

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