BigBasket

Machine Learning Intern

InternshipHybridRaipur, Chhattisgarh4 months18K/mo1 openingImmediate / Rolling

About this internship

BigBasket is hiring a Machine Learning Intern in Raipur — a hands-on, mentor-led internship built for students and 2026 freshers who want real exposure to applied machine learning rather than a certificate-only programme. About the role As a Machine Learning Intern at BigBasket, your core focus…

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Skills you'll use

PythonMachine LearningScikit-learnTensorFlowPyTorchFeature EngineeringModel EvaluationPandasNumPyMLOps Basics

What you'll do

Work on training, tuning and validating ML models on production data alongside the core team
Apply Python, Machine Learning, Scikit-learn, TensorFlow to live, production-facing problems
Document your work clearly for reviewers and stakeholders
Participate in reviews and act on the feedback you receive
Collaborate across engineering, product, design or business as required
Follow the team's quality, testing and documentation standards

Who can apply

Pursuing or recently completed B.E./B.Tech/M.Tech/M.Sc (CS, Statistics, Maths) — 2026 freshers & final-year students welcome
Working knowledge of Python, Machine Learning, Scikit-learn (or a genuine willingness to learn them)
Strong fundamentals in your discipline and clear problem-solving ability
Care with detail and the discipline to document your work
Clear written and verbal communication in English
Based in or willing to work from Raipur, Chhattisgarh

Perks & benefits

Exposure to professional tools and workflowsPre-Placement Offer (PPO) opportunityFlexible working hoursNetworking with senior professionalsInternship Certificate

Interview & selection process

3 rounds — typical selection flow for this role:

1Application screening & resume shortlisting by the talent team
2Online aptitude / domain assessment
3HR discussion — stipend, start date, expectations and offer

About BigBasket

BigBasket operates in the online grocery sector and has an active presence in Raipur. Interns here are put on real work from the start, because the team's view is that the fastest way to learn a craft is to practise it under people who already do it well — not to watch it from a distance. The work…

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