AI Model Compiler Engineer

주요 업무

- Model Compilation Pipeline: Design and implement compilers that translate AI models (ONNX, TensorFlow, PyTorch, etc.) into executable formats for AI accelerators and edge devices. - Graph Optimization: Apply operator fusion, pruning, quantization, and memory optimizations to improve model performance. - Hardware Acceleration: Optimize AI model execution on CPU, GPU, DSP, TPU, or custom AI chips (e.g., NPU, FPGA). - Intermediate Representations (IRs): Work with MLIR, TVM, XLA, Glow, or custom IRs for model transformation. - Performance Tuning: Profile and analyze models using LLVM, Halide, CUDA, OpenCL, or Metal. - Kernel Optimization: Develop low-level math libraries (SIMD, vectorized ops, matrix multiplications, tensor ops) for efficient AI inference. - Custom Operator Support: Implement new AI operators and optimize execution on target hardware. - Cross-Platform Deployment: Enable model portability across multiple architectures and backends. - AI/ML Framework Integration: Extend compiler functionality for PyTorch, TensorFlow, ONNX Runtime, and other ML frameworks. - Debugging & Benchmarking

자격 요건

- Education: Bachelor's, Master's, or Ph.D. in Computer Science, Electrical Engineering, or related fields. - Experience: 2+ years in model compilation, AI frameworks, or deep learning accelerators. - Programming Languages: C, C++, Python, and LLVM IR or MLIR. - Compiler Development: Experience with LLVM, TVM, XLA, Halide, Glow, or custom ML compilers. - Graph Transformations: Knowledge of operator fusion, loop unrolling, constant folding, quantization, and tiling techniques. - Hardware Optimization: Experience with SIMD, CUDA, OpenCL, ROCm, or low-level tensor operations. - AI Frameworks: Hands-on with TensorFlow, PyTorch, ONNX, TensorRT, TFLite, or OpenVINO. - Parallel Computing: Experience with multi-threading, vectorization (SSE/AVX), and heterogeneous computing.

우대 사항

- Neural Network Compression: Quantization-aware training (QAT), weight pruning, distillation. - Cloud & Edge AI: Deploying models on AWS Inferentia, NVIDIA Jetson, Intel Movidius, or Qualcomm AI chips. - Formal Methods & Verification: Model validation, correctness proofs, and fuzz testing for compiler robustness.

기타

- 모든 영입 과정은 수시로 진행되며, 합격자가 발생할 경우 공고가 조기 마감될 수 있습니다. - 복수의 포지션에 지원하고자 하는 분은 인터뷰 단계에서 협의하실 수 있습니다. 원활한 채용 진행을 위해 지원서는 한 번만 제출해 주세요. - 외국인 지원자분은 한국어 능력 수준과 비자 종류를 함께 명시해 주세요. - 상황에 따라 영입 프로세스가 추가 또는 생략될 수 있습니다. - 모든 인터뷰는 다대일 혹은 일대일로 진행되며 시간은 30분 ~ 1시간 가량 소요됩니다. - 제출하신 자료나 채용 프로세스 전반에서 허위 사실이 발견될 경우 채용은 취소됩니다.

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