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Yulu logo — Yulu Edge ML & Model Optimisation Intern in Howrah, West Bengal
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Edge ML & Model Optimisation Intern

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Location

Howrah, West Bengal

Work Mode

Work from Home

Duration

3 months

Stipend

₹24,000/mo

Start Date

Immediate / Rolling

Openings

3 positions

Type

Full-time

Category

Machine Learning

Apply Now — ₹24,000/moApply on Company Website ↗

About the Internship

Start your career in model compression and edge deployment with Yulu as an Edge ML & Model Optimisation Intern in Howrah. 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 Edge ML & Model Optimisation Intern…

About Yulu

Yulu offers a mentor-led Edge ML & Model Optimisation Intern in Howrah with live project work in machine learning, a stipend of ₹24,000/month and a Pre-Placement Offer opportunity for strong performers.
Howrah, West BengalVerified employer3 openings
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Skills Required

Model QuantisationModel PruningTensorFlow Lite / ONNXPythonC/C++ BasicsEmbedded DeploymentLatency ProfilingKnowledge DistillationBenchmarkingHardware Constraints

Who Can Apply

  • Final-year students and recent graduates from B.E./B.Tech (CSE/ECE/EEE), M.Tech, MCA or M.Sc (Computer Science / Electronics) who are based in or willing to work from Howrah. You should be keen to learn Model Quantisation, Model Pruning, TensorFlow Lite / ONNX on real projects and able to write clearly. Freshers are welcome — no prior work experience is required, and a strong project portfolio matters more than a long CV.

Perks

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

Interview & Selection Process

2 rounds — typical selection flow for this role:

  1. 1Application screening & resume shortlisting by the talent team
  2. 2Discussion with the hiring manager on the role and your fit
24,000/month

Stipend · 3 months

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Openings3 positions
Applicants0 applied
ModeWork from Home
CategoryMachine Learning
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