The Site Reliability Engineer for Machine Learning Systems will ensure the efficient operation of ML systems, improve resource management, oversee disaster recovery, and enhance service stability. Responsibilities include building tools for monitoring the ML infrastructure and providing global system support.
Responsibilities
- Responsible for ensuring our ML systems are operating and running efficiently for large model deployment, training, evaluation, and inference.
- Responsible for the stability of offline tasks/services in multi-data center, multi-region, and multi-cloud scenarios.
- Responsible for resource management and planning, cost and budget, including computing and storage resources.
- Responsible for global system disaster recovery, cluster machine governance, stability of business services, resource utilisation improvement and operation efficiency improvement.
- Build software tools, products and systems to monitor and manage the mL infrastructure and services efficiently.
- Be part of the global team roster that ensures system and business on-call support.
Qualifications:
- Bachelor's degree or above, majoring in Computer Science, computer engineering or related fields;
- Strong proficiency in at least one programming language such as Go/Python/Shell in Linux environment;
- Strong hands-on experience with Kubernetes and containers skills, and have ≥3 years of relevant operation and maintenance experience;
Preferred Qualifications
- Possess excellent logical analysis ability, able to reasonably abstract and split business logic, a strong sense of responsibility, good learning ability, communication ability, self-driven and good team spirit;
- Have good documentation principles and habits to be able to write and update workflow and technical documentation as required on time.
- Engage in the operation and maintenance of large-scale ML distributed systems;
- Experience in operation and maintenance of GPU servers.
Top Skills
Go
Python
Shell
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