IBM

Machine Learning Intern

InternshipIn OfficeBengaluru, Karnataka3 months37K/mo3 openingsImmediate / Rolling

About this internship

Start your career in applied machine learning with IBM as a Machine Learning Intern in Bengaluru. You will sit with a working team, use the same tools and systems they do, and own a defined piece of work from day one. About the role As a Machine Learning Intern at IBM, your core focus will be…

Skills you'll use

PythonMachine LearningScikit-learnTensorFlowPyTorchFeature EngineeringModel EvaluationPandasNumPyMLOps Basics

What you'll do

Work on training, tuning and validating ML models on real 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 Bengaluru, Karnataka

Perks & benefits

Pre-Placement Offer (PPO) opportunityFlexible working hoursNetworking with senior professionalsInternship CertificateLetter of Recommendation

Interview & selection process

3 rounds — typical selection flow for this role:

1Application screening & resume shortlisting by the talent team
2Technical interview on fundamentals and your projects
3HR discussion — stipend, start date, expectations and offer

About IBM

IBM operates in the artificial intelligence 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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