KAISHI PARTNERS PTE. LTD. Hiring! Full Time Infrastructure Engineer (GPU - Kubernetes - Distributed Systems) in Islandwide (Singapore), Earn up to SGD 12,000 - Ricebowl

We’re working with a high-growth AI infrastructure company building foundational systems powering next-generation AI products and intelligent search infrastructure.

The team is building a search engine designed for AI agents — operating large-scale distributed systems that crawl the web, train state-of-the-art embedding models, and power high-performance vector search infrastructure. On the compute side, they operate a rapidly growing multi-million-dollar H200 GPU cluster alongside large-scale distributed batch processing systems running across tens of thousands of machines.

This is a deeply technical infrastructure role focused on building the internal platform and tooling that enables the entire engineering organization to move fast at scale.

What You’ll Work On

• Build and scale Kubernetes orchestration for large GPU clusters
• Design distributed infrastructure powering large-scale AI workloads
• Scale cloud batch job systems handling map-reduce workloads across tens of thousands of machines
• Improve GPU scheduling and cluster utilization efficiency
• Build observability, reliability, and internal platform tooling for production systems
• Work on infrastructure supporting AI training, inference, crawling, and data processing at massive scale

What We’re Looking For

• Experience designing and operating large-scale infrastructure systems
• Strong hands-on experience with Kubernetes in production environments
• Familiarity with GPU clusters, distributed compute, or cloud batch processing systems
• Strong understanding of observability, reliability engineering, and system optimization
• Experience with distributed systems and performance-oriented infrastructure
• Background in high-performance engineering environments is highly valued

Nice to Have

• Experience with Ray, distributed batch systems, or large-scale orchestration platforms
• Experience optimizing GPU utilization and scheduling
• Familiarity with AWS infrastructure at scale
• Exposure to AI/ML infrastructure environments

Why This Role

• Work on infrastructure problems typically seen only at hyperscale AI companies
• Join a highly technical, low-ego engineering culture
• Opportunity to shape foundational systems from an early stage
• High ownership and ability to work on deeply challenging engineering problems
• Competitive compensation with meaningful equity upside