Site Reliability Engineer, Machine Learning Systems - Singapore

at  BYTEDANCE PTE LTD

ABOUT BYTEDANCE

Founded in 2012, ByteDance’s mission is to inspire creativity and enrich life. With a suite of more than a dozen products, including TikTok and Helo as well as platforms specific to the China market, including Toutiao, Douyin, and Xigua, ByteDance has made it easier and more fun for people to connect with, consume, and create content.

QUALIFICATIONS

Minimum Qualifications

• Bachelor’s degree or above, majoring in Computer Science, computer engineering or related fields;
• Strong proficiency in at least one programming languages such as Go/Python/Shell in Linux environment;
• Strong hands-on experience with Kubernetes and containers skills, and have more than 1 year of relevant operation and maintenance experience;

Preferred Qualifications

• Engage in the operation and maintenance of large-scale ML distributed system;
• Experience in operation and maintenance of GPU servers
• 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.

ByteDance is committed to creating an inclusive space where employees are valued for their skills, experiences, and unique perspectives. Our platform connects people from across the globe and so does our workplace. At ByteDance, our mission is to inspire creativity and enrich life. To achieve that goal, we are committed to celebrating our diverse voices and to creating an environment that reflects the many communities we reach. We are passionate about this and hope you are too

RESPONSIBILITIES

ByteDance will be prioritizing applicants who have a current right to work in Singapore, and do not require ByteDance sponsorship of a visa.

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