Location
Panaji, Goa
Work Mode
In Office
Duration
3 months
Stipend
₹31,000/mo
Start Date
Immediate / Rolling
Openings
7 positions
Type
Full-time
Category
Machine Learning
About the Internship
Start your career in model compression and edge deployment with Rainbow Children's Hospital as an Edge ML & Model Optimisation Intern in Panaji. 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 &…
About Rainbow Children's Hospital
Rainbow Children's Hospital offers a mentor-led Edge ML & Model Optimisation Intern in Panaji with live project work in machine learning, a stipend of ₹31,000/month and a Pre-Placement Offer opportunity for strong performers.
See all Rainbow Children's Hospitalinternships →Panaji, GoaVerified employer7 openings
Apply to Rainbow Children's Hospital →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 Panaji. 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
Pre-Placement Offer (PPO) opportunityFlexible working hoursNetworking with senior professionalsInternship CertificateLetter of Recommendation
Interview & Selection Process
4 rounds — typical selection flow for this role:
- 1Application screening & resume shortlisting by the talent team
- 2Online aptitude / domain assessment
- 3Technical interview on fundamentals and your projects
- 4HR discussion — stipend, start date, expectations and offer
₹31,000/month
Stipend · 3 months
Openings7 positions
Applicants0 applied
ModeIn Office
CategoryMachine Learning
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