AI/ML Engineer Intern, remote in India
About the role
This internship is for someone ready to move from coursework to real datasets. You would support our team as it builds and tests machine learning models, and see how production ML work differs from notebook exercises.
The work covers the steps around a model as well as the model itself. You would clean and prepare data, engineer features and explore what a dataset contains. Under guidance, you would then help train, evaluate and fine-tune models. You would also write scripts for data pipelines and simple evaluations, and record what each experiment showed.
Working with our engineers, you would see how a model becomes part of a product. We ask for basic Python, some familiarity with NumPy, Pandas and scikit-learn, and a grasp of core ideas such as overfitting and evaluation metrics. Curiosity and a willingness to ask questions are on the list too.
Responsibilities
- Assist with data preprocessing, feature engineering, and exploratory analysis
- Help train, evaluate, and fine-tune ML models under guidance
- Write scripts for data pipelines and basic model evaluation
- Document experiment results, learnings, and model performance
- Collaborate with engineers to understand how models integrate into products
Requirements
- Pursuing or recently completed a degree in CS, Data Science, or a related field
- Basic knowledge of Python and common ML libraries (NumPy, Pandas, scikit-learn)
- Understanding of core ML concepts (supervised/unsupervised learning, overfitting, evaluation metrics)
- Familiarity with Jupyter notebooks and data visualization tools
- Curiosity to learn and willingness to ask questions
Nice to have
- Academic projects or coursework in machine learning or deep learning
- Exposure to PyTorch or TensorFlow
- Familiarity with cloud platforms (GCP, AWS, or Azure)
How to apply
Send your resume to [email protected]. The Apply button opens an email with “Application - AI/ML Engineer Intern” already in the subject line, so we know which role you mean. If you have done machine learning coursework or a project, mention it in your email and link to a notebook or repository if you can.