Cloud Gpu Expert

1 week ago

Singapore KRUTRIM SI DESIGNS PTE. LTD. Full time

Role Overview

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We’re seeking GPU and Platform System Architect with deep expertise in GPU systems, Linux kernel architecture, and performance optimization, who will serve as the technical backbone for our silicon bring-up and cross-stack tuning. You will lead initiatives across Krutrim’s infrastructure, from enabling post-silicon validation to optimizing large-scale LLM training on multi-node GPU clusters. This role is ideal for someone who thrives in deep systems work and has a passion for cross-layer performance engineering.

Key Responsibilities

====================

Lead performance tuning and architectural optimization for GPU-based workloads, including NUMA scheduling, task preemption, memory affinity, and compute pipeline efficiency.

Architect Krutrim’s distributed training platform for LLMs, optimized for both Nvidia and Krutrim's own silicon-based clusters, with support for fine-tuning, multi-node orchestration, and cross-node tensor communication.

Work closely with silicon, firmware, and kernel teams for silicon bring-up and post-silicon validation on Bodhi chips.

Extend and upstream enhancements to OpenStack Nova, QEMU, Linux kernel, and device drivers to improve CPU and I/O determinism.

Lead benchmarking and tuning efforts to surpass AWS/GCP/Azure performance on GPU and storage-intensive AI workloads.

Partner with ML and compiler teams to drive system-level co-design, ensuring stack-level optimizations for transformer training and inference.

Preferred Qualifications

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10+ years in system performance engineering, kernel, or hardware/software co-design roles.

Expertise in GPU architecture, Linux internals (memory management, cgroups, NUMA, schedulers), and device driver development.

Hands-on experience with OpenStack Nova, QEMU/KVM, Ceph RBD, and distributed training infra (PyTorch/XLA, DeepSpeed, NCCL).

Experience in silicon bring-up, post-silicon debug, and performance validation.

Excellent debugging skills across user space, kernel space, and hardware interfaces.

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