인피니언 코리아 Internship: Edge AI (6개월)

공고명: 인피니언 코리아 Internship: Edge AI (6개월)

회사: 인피니언테크놀로지스코리아㈜

모집분야: Edge AI Internship

고용형태: 인턴

근무지: 경기 성남시 분당구 백현로 93 (수내동, 후너스빌딩) 4층

인피니언테크놀로지스코리아㈜ 인피니언 코리아 Internship: Edge AI (6개월) #WeAreIn to create tiny chips and big careers. Are you ready to take the first step in your career? Let`s shape the future together! Whether you join as a student, intern, or trainee, you`ll be part of something real - real projects, real impact, and real opportunities to grow. Are you in? 포지션 및 자격요건 Edge AI Internship ( ○명 ) Tasks: Join our 6 Months internship: Edge AI at Infineon and gain hands-on experience applying machine learning to embedded systems in our Seongnam office. As an intern, you will support the development and testing of Edge AI reference solutions, help convert and integrate machine learning models into embedded firmware, and assist with sensor data collection, preprocessing, and integration into AI pipelines. You will also contribute to system-level integration and validation across hardware and software. ㆍ Edge AI development: Support the development and testing of Edge AI reference solutions ㆍ Model integration: Convert trained machine learning models and integrate them into embedded firmware ㆍ Data collection: Support data collection and basic preprocessing for model development ㆍ Pipeline integration: Help integrate data and models into the AI pipeline ㆍ System validation: Assist with system integration and validation across hardware and software layers ㆍ Requirements: ㆍ Education: Mandatory bachelor degree or above in Electrical Engineering Computer Engineering or a related field ㆍ Embedded systems: Mandatory hands on experience with embedded systems development ㆍ Programming: Mandatory project experience in C or C++ or Python ㆍ Machine learning: Preferred basic knowledge of machine learning concepts and frameworks ㆍ Signal processing: Nice-to-have understanding of DSP or signal processing concepts ㆍ Real time systems or low power: Nice-to-have experience with RTOS or low power embedded design