Machine Learning Intern

Education Preference- Only B.tech CS/IT Graduate from the batch of 2026/2025/2024

Key Responsibilities:

• Collect and curate large-scale botanical image datasets, including scraping from online sources and organizing local image repositories with proper structure and naming conventions.
• Manually annotate plant images with precision (leaf, flower, fruit, stem, etc.) using tools like CVAT, LabelImg, or Labelme, while ensuring consistency and reviewing for quality.
• Assist in the pre-processing of image data (e.g., resizing, filtering, normalization, and augmentation) and monitor automated pipelines for correctness and completeness.
• Help validate annotations and perform sanity checks through basic machine learning inference (e.g., verifying predictions from classification or segmentation models).
• Collaborate closely with ML teams to flag edge cases, improve annotation guidelines, and maintain high data hygiene throughout the pipeline.

Requirements:

• Prior experience handling large-scale image datasets (ideally in the terabyte range) and a strong understanding of digital image formats and metadata handling.
• Knowledge of plant/botanical structures and ability to visually distinguish between different parts (e.g., leaf vs. flower), preferably with academic or project exposure.
• Proficiency in basic Python scripting — familiarity with os, pandas, opencv, Pillow and ability to automate repetitive data handling tasks.
• Understanding of image annotation workflows and tools (CVAT, LabelImg, Labelme), plus familiarity with pre-processing and augmentation techniques commonly used in computer vision.
• Basic exposure to machine learning concepts such as image classification, segmentation, and inference, along with the discipline to carry out repetitive annotation tasks with high accuracy.

Job Types: Full time, In office Internship

Contract length: 3 months