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Architecture Engineer - NPU의 핵심 아키텍처와 연산 구조를 정의하고 성능을 검증합니다. 최신 LLM 연산 패턴 분석을 통한 NPU Core 설계 성능 모델 및 사이클 레벨 시뮬레이터 개발을 통한 면적·성능·정확도 트레이드오프 분석 컴파일러 팀과 긴밀히 협업하여 HW/SW 통합 실행 모델 설계 권...
BRIDGELYT · RESEARCH & OPPORTUNITIES
퓨리오사AI Software - Software Engineer, Compiler (Kernel Optimization)
퓨리오사AI Software - Software Engineer, Compiler (Kernel Optimization) — Seoul, South Korea. 담당 업무·지원 자격·마감일과 공식 지원 링크를 확인하세요.
진행·예정 · 출처에서 확인한 정보이며 최종 조건은 공식 원문을 확인하세요.
한 줄 요약
퓨리오사AI Software - Software Engineer, Compiler (Kernel Optimization) — 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
- Analyze end-to-end LLM serving workloads and identify performance bottlenecks that can be addressed through kernel-level optimization.
- Design, implement, and optimize high-performance kernels in TCL for critical operations in Furiosa-LLM.
- Develop algorithmic techniques for efficiently supporting dynamic serving workloads.
- Integrate, benchmark, and validate optimized kernels across representative models, input shapes, and serving scenarios.
- Collaborate with compiler and serving teams to improve compiler capabilities and ensure optimized kernels work effectively within the production software stack.
지원 자격
- BS in Computer Science, Artificial Intelligence, Electrical Engineering, or a related field.
- Experience in developing low-level or performance-critical software.
- Experience in analyzing performance bottlenecks using profiling, benchmarking, and hardware performance characteristics.
- Understanding of parallel computation, memory hierarchies, and data movement on modern architectures.
우대 사항
- MS or PhD in Computer Science, Artificial Intelligence, Electrical Engineering, or a related field.
- Experience in optimizing high-performance kernels on AI accelerators (e.g., GPU, TPU) for AI products.
- Hands-on knowledge of kernel optimization techniques such as tiling, computation scheduling, operator fusion, memory layout transformation, vectorization, pipelining, and data movement optimization.
- Experience reasoning about trade-offs among parallelism, memory bandwidth, on-chip memory capacity, compute utilization, and synchronization overhead.
- Experience with accelerator programming or domain-specific kernel languages such as Triton, cuTile, or Pallas.
- talent@furiosa.ai
공식 출처와 다음 단계
공식 원문 확인원천 정보 갱신: 2026. 10. 21. PM 5:06
본문에 없는 자격·금액·일정은 임의로 보완하지 않습니다. 원문과 담당 기관에서 확인하세요.
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