Machine Learning Engineer

* THIS WOULD BE PAID INTERNSHIP FROM 2ND MONTH ONWARDS SUBJECT TO COMPLETION OF THE DELIVERABLES IN THE FIRST MONTH*

About the role

We are looking for a Machine Learning Engineer Intern with solid foundations in machine learning and deep learning, with a strong focus on structured or tabular data. You will help build, evaluate, explain, and integrate predictive models using customer, transaction, financial, and operational datasets.

Key responsibilities

• Clean and analyse tabular datasets, addressing missing values, outliers, categorical variables, and data quality issues.
• Perform exploratory data analysis and feature engineering; build classification and regression models with clear baseline comparisons.
• Develop and compare tree-based ensembles and neural networks for tabular prediction; package trained models for repeatable inference.
• Design appropriate train, validation, and test splits; identify and prevent data leakage.
• Evaluate models using suitable metrics, cross-validation, and error analysis.
• Build reproducible Python pipelines for data preparation, training, and inference. Document experiments, explain predictions, and collaborate with developers to integrate models into applications.

Required skills

• Strong Python skills, including pandas, NumPy, and scikit-learn; working knowledge of SQL and Git.
• Good knowledge of probability, statistics, and core linear algebra.
• Solid understanding of regression, classification, decision trees, random forests, and gradient boosting; hands-on experience with XGBoost, LightGBM, or CatBoost.
• Solid understanding of neural networks, backpropagation, loss functions, optimisation, and regularisation; practical experience with PyTorch or TensorFlow.
• Understanding of feature encoding, class imbalance, overfitting, cross-validation, and model evaluation.
• At least one end-to-end tabular ML project and experience training a neural network; these may be demonstrated in the same project.

Good to have

• Exposure to tabular deep learning or foundation models such as TabNet, FT-Transformer, TabPFN, or TabICL.
• Familiarity with SHAP, probability calibration, synthetic data generation, hyperparameter tuning, experiment tracking, or basic model deployment.
• Interest in credit risk, customer propensity, churn, collections, or forecasting.

Eligibility

Students or recent graduates in Data Science, Computer Science, AI or ML, Statistics, Mathematics, Engineering, or a related quantitative discipline.

Career progression

This internship offers a potential pathway to a full-time Machine Learning Engineer role. Interns who demonstrate strong technical skills, ownership, learning agility, and consistent contributions may be offered a full-time position upon successful completion, subject to performance and business requirements.

How to apply

Share your resume, GitHub or Kaggle links, and availability. Describe one tabular ML project, including the dataset, target, validation approach, evaluation metric, and your individual contribution.

Key skills

ML Engineering, Python, Machine Learning, Deep Learning, Tabular Data, pandas, NumPy, scikit-learn, XGBoost, LightGBM, CatBoost, PyTorch, TensorFlow, SQL, Feature Engineering, Model Inference