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Microsoft Member Technical Staff - Site Reliability Engineer Taiwan, Taoyuan City 525010390
16.10.2025
We’re looking for an experiencedto join our infrastructure team. In this role, you’ll blend software engineering and systems engineering to keep our large-scale distributed AI infrastructure reliable and efficient. You’ll work closely with ML researchers, data engineers, and product developers to design and operate the platforms that power training, fine-tuning, and serving generative AI models.
Responsibilities
- Reliability & Availability : Ensure uptime, resiliency, and fault tolerance of AI model training and inference systems.
- Observability : Design and maintain monitoring, alerting, and logging systems to provide real-time visibility into model serving pipelines and infra.
- Performance Optimization : Analyze system performance and scalability, optimize resource utilization (compute, GPU clusters, storage, networking).
- Automation & Tooling : Build automation for deployments, incident response, scaling, and failover in hybrid cloud/on-prem CPU GPU environments.
- Incident Management : Lead on-call rotations, troubleshoot production issues, conduct blameless postmortems, and drive continuous improvements.
- Security & Compliance : Ensure data privacy, compliance, and secure operations across model training and serving environments.
- Collaboration : Partner with ML engineers and platform teams to improve developer experience and accelerate research-to-production workflows.
Required Qualifications
- 4 years of experience in Site Reliability Engineering, DevOps, or Infrastructure Engineering roles.
Other Qualifications
- Strong proficiency in Kubernetes, Docker, and container orchestration .
- Knowledge of CI/CD pipelines for Inference and ML model deployment.
- Hands-on experience with public cloud platforms like Azure/AWS/GCP and infrastructure-as-code.
- Expertise in monitoring & observability tools (Grafana, Datadog, OpenTelemetry, etc.).
- Strong programming/scripting skills in Python, Go, or Bash .
- Solid knowledge of distributed systems, networking, and storage .
- Experience running large-scale GPU clusters for ML/AI workloads (preferred).
Preferred Qualifications
- Familiarity with ML training/inference pipelines.
- Experience with high-performance computing (HPC) and workload schedulers ( Kubernetes operators).
- Background in capacity planning & cost optimization for GPU-heavy environments.
- Work on cutting-edge infrastructure that powers the future of Generative AI.
- Collaborate with world-class researchers and engineers.
- Impact millions of users through reliable and responsible AI deployments.
- Competitive compensation, equity options, and comprehensive benefits.
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