퓨리오사AI Senior Software Engineer, Runtime

한 줄 요약

퓨리오사AI Senior Software Engineer, Runtime — 퓨리오사AI · Seoul, South Korea. 현재 상태: 진행·예정.

정보 갱신일 2026-10-21

조건·일정 확인

기관·대학

퓨리오사AI

지역·근무지

Seoul, South Korea

학력

학사 이상

담당 업무

• Develops the low-level runtime responsible for DMA-based I/O operations and kernel execution scheduling, maximizing inference throughput while minimizing end-to-end latency.
• Builds and optimizes asynchronous execution pipelines that orchestrate data movement and compute across the NPU hardware.
• Enables multi-node inference by implementing foundational communication primitives, including RDMA-based data transfer for low-latency, high-bandwidth inter-node operations.
• Develops embedded firmware (PERT) that runs on the NPU's integrated ARM core, managing on-device scheduling, synchronization, and hardware resource control.
• Profiles and tunes system-level performance across the full runtime stack — from firmware to user-space — to eliminate bottlenecks in real-world inference workloads.

지원 자격

• BS degree in Computer Science, Engineering, or a related field, or equivalent practical experience
• 5+ years of relevant industry experience or equivalent practical experience in systems programming using Rust, C, or C++
• Solid understanding of computer architecture fundamentals, including memory hierarchy, cache coherency, operating systems, DMA, interrupts, and MMIO
• Strong communication skills, with the ability to gather requirements and drive technical alignment across teams

우대 사항

• Deep expertise in low-latency runtime systems, embedded firmware development, or high-performance I/O — especially in the context of accelerator hardware.
• Experience designing and implementing low-latency asynchronous execution models and scheduling systems.
• Experience with DMA engines, scatter-gather I/O, or other zero-copy data transfer mechanisms.
• Experience developing embedded firmware for ARM-based processors (bare-metal or lightweight RTOS environments).
• Familiarity with RDMA technologies and high-performance networking for distributed or multi-node systems.
• Experience with CUDA low-level runtime internals such as CUDA Graphs, stream-based execution, and asynchronous kernel launch optimization.
• Experience with kernel-level performance optimizations (e.g., Linux kernel modules, eBPF, perf, ftrace).
• Understanding of deep learning inference workloads and their hardware execution characteristics.
• Experience with profiling and performance tuning of system software on accelerator or SoC platforms.
• talent@furiosa.ai

공식 출처와 다음 단계

원천 정보 갱신: 2026. 10. 21. PM 5:06

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