AI Data Annotator Intern (Remote)

1 month ago

InfinityWave

InternshipNegotiable

Job Overview

InfinityWave is inviting university students to join its remote Project Contribution Internship Program as an AI Data Annotator for its flagship sports analytics application, CricTrack AI. This project-based opportunity exposes participants to live Artificial Intelligence and Computer Vision workflows. Interns are responsible for preparing, labeling, and structuring high-quality datasets essential for training Machine Learning models. The role involves processing roughly 600 images daily on a specialized platform following introductory onboarding. Designed to accommodate ongoing university schedules, this fully remote position allows students across any global location to gain practical exposure to data preparation standards. It is particularly suited for proactive learners interested in Artificial Intelligence, Data Science, Machine Learning, or sports technology who wish to develop foundational industry skills alongside experienced team leads without needing prior annotation background.

• Label approximately 600 images each day utilizing the designated project platform.
• Participate in mandatory onboarding and training sessions prior to starting task execution.
• Fulfill daily image annotation allocations accurately within assigned project timelines.
• Maintain high visual accuracy and data quality by strictly following project guidelines.
• Review, update, and correct completed annotations based on quality assurance feedback.
• Maintain active communication with project coordinators regarding daily progress or technical difficulties.
• Ensure data consistency across all assigned computer vision dataset batches.
• Support machine learning model optimization through accurate data classification.

• Active enrollment as a university student (currently between Semester 1 and Semester 8).
• Foundational computer skills along with reliable, stable internet connectivity.
• Capacity to work independently and meet daily output goals of roughly 600 images.
• Strong attention to visual detail and commitment to dataset accuracy.
• Interest in Artificial Intelligence, Machine Learning, Computer Vision, or Data Science.
• Effective communication skills and a strong willingness to learn new technical tools.
• No prior annotation experience required (full workflow training provided).

This role may be suitable for candidates who are active undergraduate students looking to gain practical experience in the artificial intelligence and machine learning lifecycle. It appeals to individuals interested in computer vision, data analytics, or sports tech who need a flexible, remote project structure that fits around their university coursework. Candidates who exhibit visual precision, strong time management, and the self-discipline to meet daily output expectations without requiring direct oversight will perform well. Additionally, students with no previous technical work experience who are eager to learn structured data preparation processes will find this guided internship program beneficial.

• Complete onboarding guidance and practical training on annotation workflows and quality standards.
• Direct industry exposure working on a live AI sports analytics product (CricTrack AI).
• Flexible remote project contribution structure suitable for balancing university schedules.

Applicants may want to highlight their academic major, current semester level, and interest in artificial intelligence or computer vision applications on their resume. Emphasizing core personal capabilities such as visual attention to detail, time management, and a track record of meeting deadlines will help demonstrate readiness for daily production goals. If you have completed academic projects, coursework, or self-directed learning in data handling, computer science, or sports analytics, brief references to these experiences can strengthen your profile. Be sure to highlight your availability for remote project commitments and your enthusiasm for participating in initial training sessions.

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