Site Reliability Engineer, Machine Learning Systems - Singapore Technology - Backend Singapore [...]

ByteDance; Singapore; On-site; SGD 60,000 - 80,000; Full time

Job summary

ByteDance in Singapore is seeking a Site Reliability Engineer for their Machine Learning Systems team. You'll ensure operational efficiency, stability, and disaster recovery of ML systems while collaborating with a global team.

Ideal candidates possess a Bachelor's degree in Computer Science and proficiency in programming languages such as Go, Python, or Shell, alongside hands-on experience with Kubernetes. This role offers the opportunity to enhance your coding and performance analysis skills within a cutting-edge AI research environment.

Qualifications

• Bachelor's degree or above in Computer Science, computer engineering or related fields.
• Strong programming skills in Go, Python, or Shell in a Linux environment.
• Experience with Kubernetes and containers, with over 1 year in operation and maintenance.

Responsibilities

• Ensure ML systems operate efficiently for deployment, training, and inference.
• Manage stability of systems across multiple data centers and clouds.
• Oversee global system disaster recovery and resource planning.

Skills

Proficiency in Go/Python/Shell

Kubernetes expertise

Linux environment experience

Resource management

Education

Bachelor's degree in Computer Science or related fields

Job description

Site Reliability Engineer, Machine Learning Systems - Singapore

Employment Type: Regular

Job Code: A247694

Responsibilities

The ByteDance Large Model Team is committed to developing the most advanced AI large model technology in the industry, becoming a world‑class research team, and contributing to technological and social development. The Large Model Team has a long‑term vision and determination in the field of AI, with research directions covering NLP, CV, speech, and other areas. Relying on the abundant data and computing resources of the platform, the team has continued to invest in relevant fields and has launched its own general large model, providing multi‑modal capabilities. The Machine Learning (ML) System sub‑team combines system engineering and the art of machine learning to develop and maintain massively distributed ML training and inference system/services around the world, providing high‑performance, highly reliable, scalable systems for LLM/AIGC/AGI. In our team, you'll have the opportunity to build the large‑scale heterogeneous system integrating with GPU/NPU/RDMA/Storage and keep it running steadily and reliably, enrich your expertise in coding, performance analysis and distributed system, and be involved in the decision‑making process. You'll also be part of a global team with members from the United States, China and Singapore working collaboratively towards unified project direction.

• Responsible for ensuring ML systems are operating and running efficiently for large model deployment, training, evaluation, and inference
• Responsible for the stability of offline tasks/services in multiple 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

Minimum 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 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