ML Performance Optimization Engineer
Remote · Pangyori, Gyeonggi-do, Republic of Korea
Pangyori, Gyeonggi-do, Republic of KoreaRemoteFull TimeMid LevelMasters DegreeUnknown
Type
Full Time
Level
Mid Level
Education
Masters Degree
Company size
Unknown
Job Summary
ML Performance Optimization Engineer for Autonomous Driving divisions. Optimize AI models and system performance focusing on GPU/NPU acceleration, enable seamless deployment and performance improvements on vehicle platforms, collaborate with AI model and software teams to validate models, profile systems, and automate performance analysis for autonomous driving systems.
Required Qualifications
- 석사 학위 이상 또는 이에 준하는 경력 보유자(인공지능/머신러닝/딥러닝 관련 분야)
- 머신러닝 및 딥러닝에 대한 이해
- CPU, GPU, NPU 기반 Low-level 성능 최적화 경험
- C/C++, Python, Shell Script 개발 경험
- Linux, QNX, RTOS 환경에서의 개발 경험
- 시스템 성능 분석 및 Profiling 경험
- Master’s degree or higher in Artificial Intelligence, Machine Learning, Deep Learning, or related fields, or equivalent practical experience
- Strong understanding of machine learning and deep learning systems
- Experience with low-level performance optimization for CPU, GPU, and NPU environments
- Proficiency in C/C++, Python, and shell scripting
- Development experience in Linux, QNX, or RTOS environments
- Experience with system profiling and performance analysis
- Preferred Qualifications include optimization of ML workloads, linear algebra routines, image processing/vision/robotics algorithms, CUDA/MKL/SIMD/NEON
- NVIDIA-based vehicle-platform experience
- Autonomous Driving AI model optimization experience
Desired Qualifications
- Master’s degree or higher in Artificial Intelligence, Machine Learning, Deep Learning, or related fields
- Experience with CPU/GPU/NPU low-level performance optimization
- Proficiency in C/C++, Python, and shell scripting
- Development experience in Linux, QNX, or RTOS environments
- Experience with system profiling and performance analysis
- Experience with GPU/NPU acceleration for deep learning models
- CUDA, MKL, SIMD, NEON optimization techniques (preferred)
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