Key Responsibilities: - Responsible for Dataset Creation & Annotation, Collect, organize, and annotate data for real-world business scenarios using tools (e.g., Label Studio, CVAT), maintain dataset versions and perform quality checks. - Responsible for Model Training & Tuning, Train baseline AI/ML models (e.g., classification, detection) using frameworks like PyTorch/TensorFlow, assist in hyperparameter tuning and document experimental results - Assist Model Evaluation & Deployment, support deployment of trained models to real-world applications, assist in quantitative evaluation of model performance (e.g., accuracy, inference speed) - Assist in optimizing robot control algorithms (e.g., path planning, motion control) and participate in simulation/field testing, contribute to basic development tasks using ROS (Robot Operating System) or related tools. - Assist Test Automation in Infotainment System, develop Python-based automation scripts to validate infotainment systems and analyze test results and generate summary reports. Requirements: - Programming Fundamentals • Proficiency in Python (basic syntax, libraries like NumPy/Pandas). • Familiarity with Linux command-line operations. - Machine Learning Basic • Understanding of supervised learning concepts (train/validation splits, loss functions). • Coursework or self-study experience with at least one deep learning framework (PyTorch/Keras/TensorFlow). - Tool Adaptability: Ability to quickly learn annotation tools (e.g., Label Studio) or collaborative tools (Git/Jira). - Academic Background: Pursuing a degree in Computer Science, Automation Engineering, Robotics, Vehicle Engineering, or related fields.