AI Engineer - Internship (WFH & Part Time)

About the internship:Selected intern's day-to-day responsibilities include:

1. Collect and curate short audio clips across a defined set of everyday sound categories and environments (e.g., speech, traffic, kitchen sounds, nature, indoor/outdoor scenes).

2. Apply structured labels to audio clips according to Eventag's sound taxonomy and a separate activity/scene label layer.

3. Organize and document datasets in a structured, reproducible format for model training.Who can apply:Only those candidates can apply who:are available for the work from home job/internshipcan start the work from home job/internship between 24th Sep'26 and 29th Oct'26are available for duration of 3 monthshave relevant skills and interests* Women wanting to start/restart their career can also apply.Stipend:INR₹ 2,000 - 2,500 /monthDeadline:[hidden]:59:59Other perks:Certificate, Letter of recommendation, Flexible work hours, 5 days a weekSkills required:Machine Learning, Data Extraction, Data Cleaning and Data AnnotationOther Requirements:1. Students in computer science, linguistics, cognitive science, audio engineering, or related fields are especially encouraged to apply.2. Interest in gaining direct, hands-on experience in building a real-world ML training dataset from scratch at an early-stage startup.3. Interest in mentorship from the founding team on audio ML, context-aware computing, and startup operations.About Company:Calyp Innovations Private Limited is the parent company of Eventag.ai. We help researchers study EEG and other biosignals in a real-world setting.

Responsibilities

Selected intern's day-to-day responsibilities include:

1. Collect and curate short audio clips across a defined set of everyday sound categories and environments (e.g., speech, traffic, kitchen sounds, nature, indoor/outdoor scenes).

2. Apply structured labels to audio clips according to Eventag's sound taxonomy and a separate activity/scene label layer.

3. Organize and document datasets in a structured, reproducible format for model training.