Slice

Machine Learning Intern

InternshipIn OfficeKolkata, West Bengal4 months22K/mo1 openingImmediate / Rolling

About this internship

Slice has an open Machine Learning Intern position in Kolkata 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 Slice, your…

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 Kolkata, West Bengal

Perks & benefits

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

Interview & selection process

4 rounds — typical selection flow for this role:

1Application screening & resume shortlisting by the talent team
2Team-fit / behavioural interview with the team
3Online aptitude / domain assessment
4HR discussion — stipend, start date, expectations and offer

About Slice

Slice operates in the fintech sector and has an active presence in Kolkata. 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 at…

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