Texas Instruments India

AI / Machine Learning Intern

InternshipIn OfficeNoida, Uttar Pradesh2 months29K/mo4 openingsImmediate / Rolling

About this internship

Texas Instruments India has an open AI / Machine Learning Intern position in Noida for motivated freshers and final-year students. You will work on live applied AI and machine learning projects and finish with something you can genuinely talk about in interviews. About the role As an AI / Machine…

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Skills you'll use

PythonMachine LearningDeep LearningPyTorch / TensorFlowScikit-learnPandasFeature EngineeringModel EvaluationSQLMLOps Basics

What you'll do

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

Perks & benefits

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

Interview & selection process

2 rounds — typical selection flow for this role:

1Application screening & resume shortlisting by the talent team
2Team-fit / behavioural interview with the team

About Texas Instruments India

Texas Instruments India operates in the semiconductors sector and has an active presence in Noida. 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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