Staff AI Infrastructure Systems Engineer

Cloudera; Singapore; On-site; SGD 180,000 - 260,000; Full time

Benefits offered by this job

Generous PTO Policy

Flexible WFH Policy

Mental & Physical Wellness programs

Phone and Internet Reimbursement

Continued Career Development

Job summary

Cloudera in Singapore is seeking a Staff Systems Engineer – AI Infrastructure to design and implement scalable GPU-backed infrastructure for AI workloads across on‑premises and cloud environments. You will translate complex requirements into concrete engineering solutions and drive hands-on delivery across the stack.

This role emphasizes experimentation, benchmarking, and collaboration with product teams to improve performance, reliability, and scalability of AI platforms.

Qualifications

• 8+ years in systems software, distributed infrastructure, platform engineering or related field.
• Hands-on experience with production GPU-based infrastructure supporting AI workloads.
• Ability to investigate ambiguous problems and validate solutions via experimentation and prototyping.

Responsibilities

• Investigate AI workloads across heterogeneous GPU environments and validate concepts.
• Develop infrastructure for production AI workloads including inference and serving.
• Diagnose Linux, GPU runtimes, containers, networking, storage issues and optimize.
• Investigate GPU resource efficiency and sharing/partitioning.
• Build tooling for diagnostics, benchmarking, deployment validation.
• Translate infrastructure findings into requirements and product improvements.

Skills

Distributed systems

Linux

Containers

GPU infrastructure

Performance engineering

Cloud integration

Tools

Kubernetes

NVIDIA MIG

NVIDIA NIM

Benchmarking tools

Job description

Cloudera in Singapore is seeking a Staff Systems Engineer – AI Infrastructure to design and implement scalable GPU-backed infrastructure for AI workloads across on‑premises and cloud environments. You will translate complex requirements into concrete engineering solutions and drive hands-on delivery across the stack.

This role emphasizes experimentation, benchmarking, and collaboration with product teams to improve performance, reliability, and scalability of AI platforms.