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Machine Learning Intern (Paid)
Company: ZenithByte
Location: Remote
Duration: 3 Months
Opportunity: Full-time role based on performance + Certificate of Internship
Application Deadline: 28th November 2025
About ZenithByte
ZenithByte is committed to empowering students and graduates by providing real-world exposure to Machine Learning and Data Science. We focus on bridging the gap between academic learning and industry expectations through live projects, expert mentors, and performance-driven career opportunities.
Role Overview
As a Machine Learning Intern at ZenithByte, you will work with real datasets, build predictive models, and explore cutting-edge AI tools. This internship will help you enhance your analytical and technical skills while contributing to impactful projects in automation and data-driven decision-making.
Responsibilities
Design, train, and optimize machine learning models for real-world applications.
Perform data preprocessing, feature engineering, and model evaluation.
Develop predictive algorithms and implement data-driven solutions.
Work with ML frameworks such as TensorFlow, PyTorch, and Scikit-learn.
Document findings, insights, and model performance in technical reports.
Requirements
Enrolled in or recently graduated from Computer Science, AI, Data Science, or related fields.
Strong understanding of ML algorithms and data analysis concepts.
Proficient in Python (preferred) or R with practical experience in ML libraries.
Familiarity with data visualization tools and model evaluation techniques.
Strong analytical thinking, communication, and teamwork skills.
Benefits
Stipend: 7,500 - 15,000 (Performance-Based) (Paid)
Hands-on experience with real-world ML projects.
Certificate of Internship & Letter of Recommendation.
Opportunity for full-time placement based on performance.
Build a strong, industry-ready ML portfolio.
Equal Opportunity Statement
ZenithByte is an equal-opportunity employer. We are committed to diversity and inclusion and welcome applicants from all backgrounds and experiences.
Job ID: 133654943