Razorpay

AI Research Intern

InternshipIn OfficeThane, Maharashtra2 months26K/mo4 openingsImmediate / Rolling

About this internship

Start your career in AI research and algorithm design with Razorpay as an AI Research Intern in Thane. 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 an AI Research Intern at Razorpay, your core focus will…

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Skills you'll use

Algorithm DesignDeep LearningPyTorchAI EthicsAcademic Writing & PublishingLiterature ReviewExperiment DesignPythonMathematics for MLReproducible Research

What you'll do

Work on running structured experiments against open research problems alongside the core team
Apply Algorithm Design, Deep Learning, PyTorch, AI Ethics 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 / Mathematics) or Ph.D. scholars — 2026 freshers & final-year students welcome
Working knowledge of Algorithm Design, Deep Learning, PyTorch (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 Thane, Maharashtra

Perks & benefits

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

Interview & selection process

2 rounds — typical selection flow for this role:

1Application screening & resume shortlisting by the talent team
2Technical interview on fundamentals and your projects

About Razorpay

Razorpay operates in the payments & banking technology sector and has an active presence in Thane. 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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