Senior Site Reliability Engineer (Seed Model - Infrastructures - SRE)

What you'll do

• Optimize training and inference stability, performance, and resource scheduling across heterogeneous compute clusters.
• Plan and operate high-speed networks, storage systems, and compute capacity; improve utilization and cost efficiency.
• Build monitoring, alerting, and automated remediation, and partner with teams on MLOps solutions and engineering practices.

What they ask for

• Bachelor's degree or above in Computer Science, Software Engineering, or a related field.
• 3+ years of experience in large-scale machine learning / generative AI MLOps or closely related infrastructure work.
• Proficiency in Python and/or Go, Linux operations and development, and shell scripting; expertise in Docker / Kata and Kubernetes.
• Hands-on experience with heterogeneous compute clusters, resource scheduling, and large-scale generative AI training or inference systems.

Nice to have

• End-to-end experience productionizing large-scale generative AI models.
• Experience with multi-cloud or hybrid-cloud capacity planning, distributed training or inference optimization, network or storage tuning, or chaos exercises.

Tools

Python, Go, Linux, Docker, Kata, Kubernetes, RDMA, GPU / NPU / XPU

AI focus

Reliability, performance optimization, scheduling, and capacity operations for large-scale generative AI training and inference infrastructure.