Navi

Machine Learning Intern

InternshipIn OfficeBengaluru, Karnataka2 months33K/mo1 openingImmediate / Rolling

About this internship

Navi has an open Machine Learning Intern position in Bengaluru for motivated freshers and final-year students. You will work on live applied machine learning projects and finish with something you can genuinely talk about in interviews. About the role As a Machine Learning Intern at Navi, your…

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Skills you'll use

PythonMachine LearningScikit-learnTensorFlow or PyTorchFeature EngineeringModel EvaluationPandasNumPySQLMLOps 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 or PyTorch 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 Bengaluru, Karnataka

Perks & benefits

Live project experienceExposure to professional tools and workflowsPre-Placement Offer (PPO) opportunityFlexible working hoursNetworking with senior professionals

Interview & selection process

2 rounds — typical selection flow for this role:

1Application screening & resume shortlisting by the talent team
2Discussion with the hiring manager on the role and your fit

About Navi

Navi operates in the fintech & digital banking sector and has an active presence in Bengaluru. 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…

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