Job Description:
- Research, design, and develop machine learning models applied to various products, including hardware devices with different configurations (Cloud, Fog, Edge).
- Optimize machine learning models to improve performance while maintaining accuracy, stability, and meeting specified requirements.
- Deploy machine learning models across devices with different operating systems; convert models for deployment on AI chipsets (e.g., DLC, TensorRT).
- Analyze model output results, identify strengths and weaknesses, highlight issues to be resolved, and enhance model features.
Job Requirements:
- Bachelor's degree or higher in Information Technology, Computer Science, Mathematics-Informatics, Electronics and Telecommunications, or other related engineering fields with a grade of Good or above.
- Strong mathematical foundation, including matrix algebra, probability and statistics, optimization, machine learning, and deep learning.
- Experience in machine learning and deep learning projects with proficiency in traditional machine learning algorithms (supervised and unsupervised) and deep learning models.
- Knowledge of at least one domain such as image processing or audio processing.
- Practical experience with algorithms such as CNN, GCNN, RNN, LSTM; skilled in using backbone architectures like VGG, ResNet, MobileNet; proficient with object detection and classification models such as YOLO, SSD, CenterNet, ArcFace; adept in transfer learning.
- Flexible in using various loss functions; capable of analyzing, researching, and developing new loss functions to enhance algorithm functionality.
- Proficient in Python programming language; experienced in model conversion using SNPE, TensorRT, Tensor Core, etc.
Preferred Qualifications:
- Experience in programming with CUDA or C/C++.
- Candidates with previous experience in equivalent roles at reputable domestic or international companies or organizations.
