Artificial Intelligence Internship Opportunity

Artificial Intelligence Internship (Remote, Unpaid)

About The Opportunity

Internship Duration

Role & Responsibilities

• Implement and run experiments: build reproducible training pipelines for supervised or self-supervised tasks using Python and deep learning frameworks.
• Prepare and curate datasets: perform cleaning, augmentation, and feature engineering for model readiness.
• Train & evaluate models: tune hyperparameters, track metrics, and perform error analysis to improve baseline performance.
• Prototype end-to-end components: integrate models into lightweight inference scripts or APIs and validate on real-world sample data.
• Document results and collaborate: produce clear experiment logs, visualizations, and present findings to the engineering team.
• Follow engineering best practices: write tested, version-controlled code and contribute to team code reviews.
  

Skills & Qualifications

Must-Have

• Currently enrolled in or recently graduated from a Bachelors/Masters program in Computer Science, EE, Math, Statistics, or a related field.
• Proficient in Python and libraries such as NumPy and pandas; able to write clean, reproducible scripts.
• Hands-on experience with ML/DL frameworks (PyTorch or TensorFlow) and basic model training workflows.
• Strong understanding of ML fundamentals: loss functions, optimization, overfitting, and evaluation metrics.
• Good problem-solving, data analysis, and communication skills for remote collaboration.
  

Preferred

• Experience with NLP (transformers, tokenization) or computer vision (CNNs, augmentation) projects.
• Familiarity with Git, Linux CLI, and experiment-tracking tools (Weights & Biases, TensorBoard).
• Prior contributions or projects on GitHub, Kaggle notebooks, or research/prototype demos.
  

Benefits & Culture Highlights

• Fully remote internship open to candidates across India with flexible working hours.
• Direct mentorship from senior engineers, hands-on project ownership, and visibility to leadership.
• Practical experience in building ML components for production-like environments and a certificate of completion.
  

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