Emeritus

Research Intern (AI/ML)

InternshipWork from HomeKolhapur, Maharashtra4 months26K/mo1 openingImmediate / Rolling

About this internship

Start your career in applied AI and machine learning with Emeritus as a Research Intern (AI/ML) in Kolhapur. 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 Research Intern (AI/ML) at Emeritus, your core…

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Skills you'll use

PythonDeep LearningLiterature ReviewPyTorchExperiment DesignStatisticsPaper WritingModel EvaluationGitReproducibility

What you'll do

Work on training, evaluating and deploying models alongside the core team
Apply Python, Deep Learning, Literature Review, 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/MCA/M.Sc (CS, Statistics, Maths) — 2026 freshers & final-year students welcome
Working knowledge of Python, Deep Learning, Literature Review (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 Kolhapur, Maharashtra

Perks & benefits

Letter of RecommendationMentorship from a named mentorLive project experienceExposure to professional tools and workflowsPre-Placement Offer (PPO) opportunity

Interview & selection process

3 rounds — typical selection flow for this role:

1Application screening & resume shortlisting by the talent team
2Team-fit / behavioural interview with the team
3HR discussion — stipend, start date, expectations and offer

About Emeritus

Emeritus operates in the edtech sector and has an active presence in Kolhapur. 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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