Zerodha

Machine Learning Intern

InternshipHybridPune, Maharashtra3 months44K/mo3 openingsImmediate / Rolling

About this internship

Join Zerodha in Pune as a Machine Learning Intern. This entry-level internship is designed for students and recent graduates who learn best by building, shipping and getting real feedback on applied machine learning work. About the role As a Machine Learning Intern at Zerodha, your core focus will…

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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 Pune, Maharashtra

Perks & benefits

Mentorship from a named mentorLive project experienceExposure to professional tools and workflowsPre-Placement Offer (PPO) opportunityFlexible working hours

Interview & selection process

3 rounds — typical selection flow for this role:

1Application screening & resume shortlisting by the talent team
2Practical assignment or case study with a short walkthrough
3HR discussion — stipend, start date, expectations and offer

About Zerodha

Zerodha operates in the stock broking sector and has an active presence in Pune. 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 runs…

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