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ML Performance Optimization Engineer
42dotML Performance Optimization Engineer optimizing GPU/NPU-based AI models for autonomous driving systems at 42dot. Collaborating with AI and software teams to enhance system performance and validation.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Expertise in optimizing deep learning models for GPU/NPU environments, with a strong focus on performance validation, system profiling, and low-level optimization in AI applications. Proficient in developing automation tools for performance analysis and evaluation metrics in automotive AI systems.
Highest-signal resume keywords
Deep Learning Model OptimizationPerformance Validation and System ProfilingLow-Level Performance OptimizationC/C++ and Python DevelopmentLinux and RTOS Development Experience
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
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Hard Skills
Deep LearningMachine LearningPerformance AnalysisSystem ProfilingLow-Level OptimizationC/C++ ProgrammingPython ProgrammingShell ScriptingGPU OptimizationNPU Optimization
Tools & Technologies
LinuxQNXRTOSPerformance Analysis ToolsAutomation Tools
Certifications & Qualifications
Master's Degree in AI or Related Field
Industry Keywords
Artificial IntelligenceAutomotive AINeural Network Accelerator
Tech Stack
Tools & technologiesLinuxPythonRTOS
About the role
Key responsibilities & impact- GPU/NPU 기반 딥러닝 모델 최적화 수행
- 딥러닝 모델의 성능 검증 및 시스템 프로파일링 수행
- CPU, GPU, Neural Network Accelerator 성능 분석 및 최적화
- 시스템 성능 분석 툴 및 성능 평가 지표 개발 자동화
- 차량 환경에서의 AI 모델 동작 최적화 및 배포 지원
Requirements
What you’ll need- 인공지능, 머신러닝/딥러닝 관련 분야 석사 학위 이상 또는 이에 준하는 경력 보유자
- 머신러닝 및 딥러닝에 대한 이해
- CPU, GPU, NPU 기반 Low-level 성능 최적화 경험
- C/C++, Python, Shell Script 기반 개발 경험
- Linux, QNX, RTOS 환경에서의 개발 경험
- 시스템 성능 분석 및 Profiling 경험
Benefits
Comp & perks- 국가보훈대상자 및 취업보호 대상자는 관계법령에 따라 우대합니다.
- 장애인 고용 촉진 및 직업재활법에 따라 장애인 등록증 소지자를 우대합니다.
- 3개월의 수습기간이 적용될 수 있습니다.