IBM

Computer Vision Intern

InternshipIn OfficeBengaluru, Karnataka4 months36K/mo1 openingImmediate / Rolling

About this internship

IBM has an open Computer Vision Intern position in Bengaluru for motivated freshers and final-year students. You will work on live computer vision projects and finish with something you can genuinely talk about in interviews. About the role As a Computer Vision Intern at IBM, your core focus will…

Skills you'll use

PythonComputer VisionOpenCVPyTorchCNNsObject DetectionImage SegmentationData AugmentationModel OptimisationNumPy

What you'll do

Work on building image and video models for detection and recognition alongside the core team
Apply Python, Computer Vision, OpenCV, 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, Electronics) — 2026 freshers & final-year students welcome
Working knowledge of Python, Computer Vision, OpenCV (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

Networking with senior professionalsInternship CertificateLetter of RecommendationMentorship from a named mentorLive project experience

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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