리벨리온 NPU Software Engineer - SDK

한 줄 요약

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

정보 갱신일 2026-10-21

조건·일정 확인

기관·대학

리벨리온

지역·근무지

Rebellions | Korea

학력

석사 이상

담당 업무

• SDK Ecosystem & Release Pipeline: Design, build, and operate CI/CD pipelines that automate and scale the SDK release process
• Model Deployment & Porting: Port and optimize the latest deep learning models (LLM, Multi-modal, etc.) onto Rebellions NPU, analyze performance bottlenecks, and validate numerical parity against GPU reference implementations when needed
• Framework Integration: Integrate the SDK tightly with the open-source inference and serving ecosystem, including Hugging Face Transformers, Diffusers, vLLM, and to maximize the developer experience
• Verification & Debugging: Design model-feeding frameworks and debugging utilities to ensure functional correctness and long-term stability of the SDK
• Developer Documentation: Author and maintain developer-facing documentation — API references, tutorials, model support matrices, and release notes — and operate the documentation build and publishing pipeline for external developers and partners
• 채용 관련 문의사항은 아래 메일 주소로 문의바랍니다.
• recruit@rebellions.ai

지원 자격

• Master’s degree or above in Computer Science, Electrical Engineering, or a related field
• Deep working knowledge of CI/CD tools such as GitHub Actions, Airflow, and Buildkite
• Deep understanding of modern deep learning model architectures, including LLMs (Transformer family), Multi-modal, Vision, and Speech
• Strong familiarity with PyTorch internals, including model customization and graph transformations
• High-performance software development skills in Python and Modern C++ (C++17 or later)
• Experience using and integrating major open-source libraries such as Hugging Face (Transformers, Diffusers) and vLLM
• Hands-on experience with Python and Kubernetes-based environments for deploying and troubleshooting AI inference workloads in production

우대 사항

• 5+ years of relevant industry experience, or equivalent practical expertise
• Experience optimizing and deploying models on specific hardware using CUDA, TensorRT, TensorRT-LLM, MLIR, Triton, or TVM
• Experience building scalable ML infrastructure with Docker and Kubernetes
• Experience in AI Harness Engineering – designing environments where AI agents operate autonomously within defined constraints, feedback loops, and system contexts
• Contributions to open-source projects as a maintainer or regular contributor
• Understanding of the internals of other frameworks such as JAX or TensorFlow

공식 출처와 다음 단계

원천 정보 갱신: 2026. 10. 21. PM 5:06

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