Global ML MLOps & SRE Engineer — Inference & Training

United States Digital Space LLC; Singapore; On-site; SGD 60,000 - 90,000; Full time

Job summary

United States Digital Space LLC in Singapore is seeking a Machine Learning MLOps Engineer Graduate for the Global SRE team starting in 2027. The role focuses on defining SLOs for online inference, ensuring reliability and performance of production systems, while also strengthening offline training pipelines and resource optimization for ML workloads.

You will contribute to deploying new ML frameworks and GPU platforms, building robust MLOps platforms, and driving observability and automation

Qualifications

• Bachelor's or Master's degree in Computer Science, Software Engineering, or related discipline
• Expertise in Linux OS, networking and storage
• Experience programming in Python, Go, C, C++, or Java
• Experience troubleshooting applications or production operations
• Strong communication and ownership mindset

Responsibilities

• Define and drive Service Level Objectives (SLOs) for online ML inference systems ensuring reliability and performance of large-scale production inference services
• Ensure reliability and operational excellence of offline ML training pipelines and improve training job success rates
• Drive infrastructure capacity planning and resource management for ML workloads, optimize GPU/CPU utilization
• Lead enablement of new ML frameworks and GPU platforms for large-scale production deployment while maintaining model quality and business performance
• Design, build, and maintain MLOps platforms and automation tools including quota management, diagnostics, monitoring, and observability

Skills

Programming languages: Python, Go, C,C

Education

Bachelor's / Master's in Computer Science or Software Engineering

Job description

United States Digital Space LLC in Singapore is seeking a Machine Learning MLOps Engineer Graduate for the Global SRE team starting in 2027. The role focuses on defining SLOs for online inference, ensuring reliability and performance of production systems, while also strengthening offline training pipelines and resource optimization for ML workloads.

You will contribute to deploying new ML frameworks and GPU platforms, building robust MLOps platforms, and driving observability and automation