Responsibilities:

Define and drive Service Level Objectives (SLOs) for online machine learning inference systems, ensuring the reliability, availability, and performance of large-scale production inference services;
Ensure the reliability and operational excellence of offline machine learning training pipelines, continuously improving training job success rates;
Drive infrastructure capacity planning and resource management for machine learning workloads, ensuring compute resources meet evolving business demands while continuously improving GPU and CPU utilization through performance optimization;
Lead the enablement of new machine learning frameworks and GPU platforms, driving large-scale production deployment while maintaining model quality and business performance;
Design, build, and maintain MLOps platforms and automation tools, including quota management, job diagnostics, monitoring and observability, resource management, and operational tooling.

Qualifications Minimum Qualifications:

Currently pursuing a Bachelor's degree in Computer Science or a related discipline.
Expertise in Linux operating systems, networking, storage.
Experience programming in at least one of the following programming languages: Python, Go, C, C++, or Java.
Experience in troubleshooting application issues, or production operations.
Effective communication skills and a sense of ownership and drive.

Preferred Qualifications:

Experience in SRE internship for large-scale advertising, recommendation, search systems, or large-scale cloud platforms.

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