리벨리온 NPU Software Engineer - Runtime

한 줄 요약

리벨리온 NPU Software Engineer - Runtime — 리벨리온 · Rebellions | Korea. 현재 상태: 진행·예정.

정보 갱신일 2026-10-21

조건·일정 확인

기관·대학

리벨리온

지역·근무지

Rebellions | Korea

학력

학사 이상

담당 업무

• Design and implement the RBLN runtime module that interfaces with compiler and driver components, including the graph executor and runtime APIs, to enable ML model deployment through the RBLN SDK
• Architect and maintain native PyTorch execution support within the runtime, including torch.compile integration and RBLN compiler toolchains, to enable seamless NPU acceleration with minimal user-side code changes
• Design and implement a user-facing profiler that provides actionable performance insights, delivered as part of the RBLN SDK
• Develop and extend vLLM to enhance inference performance on NPUs, including support for key vLLM features such as advanced memory management, parallelism, and dynamic batching
• Design and optimize distributed inference across multi-NPU setups, including collective communication operations (CCL) to support various parallelism strategies
• Conduct benchmarking and profiling to evaluate runtime system performance and implement optimizations to improve overall system efficiency
• Collaborate with ML engineers and infrastructure teams to deploy and scale inference services
• 채용 관련 문의사항은 아래 메일 주소로 문의바랍니다.
• recruit@rebellions.ai

지원 자격

• Over 5 years of experience in software engineering, with significant work on ML frameworks, inference runtimes, or AI accelerator toolchains in production environments
• Bachelor's degree or higher in Computer Science, Electrical Engineering, or a related field
• Strong proficiency in C++ and Python
• Strong understanding of deep learning fundamentals and LLM architectures, including Transformer-based models, generative AI, and inference optimization techniques
• Hands-on experience with LLM serving frameworks (e.g., vLLM, TensorRT-LLM)
• Solid understanding of model optimization techniques (tensor parallelism, KV cache optimizations, memory-efficient execution)
• Familiarity with system software components, including compilers, runtimes, drivers, and firmware
• Familiarity with hardware acceleration (GPUs, NPUs, TPUs) and efficient memory management techniques
• Strong debugging and performance profiling skills for high-throughput inference environments
• Ability to work effectively across compiler, driver, and ML engineering teams
• Excellent written and verbal communication skills

우대 사항

• Practical experience with AI accelerator runtimes and driver APIs (e.g., GPUs)
• Direct contribution or production experience with ML frameworks and serving systems such as PyTorch, vLLM, SGLang, TensorRT, and TensorRT-LLM
• Understanding of torch.compile and graph optimizations
• Strong understanding of operating systems, resource management, and high-performance computing concepts
• Advanced proficiency in modern C++ for developing efficient, high-performance systems
• Experience with multithreading and parallel programming
• Experience deploying LLMs in distributed environments

공식 출처와 다음 단계

원천 정보 갱신: 2026. 10. 21. PM 5:06

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