Main Responsibilities

• Assist in building and maintaining training datasets: data collection, cleaning, deduplication, and checking annotation quality.
• Run and reproduce existing training scripts: execute training under guidance, record logs, and organize training results (loss, metrics).
• Implement and tune data augmentation and preprocessing pipelines (implement or adjust common augmentations).
• Help integrate annotation formats (COCO, Pascal, YOLO) and evaluation scripts, and produce standardized validation reports.
• Support preparation of synthetic data, data-augmentation workflows (using simple scripts or toolchains).
• Under the guidance of senior engineers, perform basic model export and packaging (e.g., ONNX, TorchScript or similar simple formats).
• Write or supplement experiment logs, training-step instructions, and data documentation to ensure reproducibility.

Required Qualifications

• Master’s student or recent graduate; majors in Computer Science, Software, Electrical, Computer Engineering, Automation, Machine Learning, or related fields preferred.
• Proficient in Python and able to write scripts for data processing (basic operations with Pandas, NumPy, OpenCV).
• Basic familiarity with a deep-learning framework (PyTorch or TensorFlow); able to run training, evaluation scripts provided by others.
• Understanding of common image data formats and annotation formats (COCO, Pascal VOC, YOLO).
• Good habit of keeping experiment records; able to write clear reproduction steps in Markdown or plain text.
• Basic Linux , command-line skills; experience using Git for version control.
• Team-player with quick learning ability.