InSiSo Technologies Private Limited | Bengaluru

Paid internship · ₹10,000/month · Fast-track to full time for strong performers. Must be willing to relocate to Bengaluru.

About

InSiSo

InSiSo builds InSiSoNet — a proprietary, hardware-agnostic vision AI architecture that runs entirely on-device, with no cloud dependency. Our models are trained from scratch on our own architecture and deployed on constrained edge silicon, where every millisecond counts.

Bosch Rexroth ctrlX Partner · NVIDIA Inception · Intel ISV Partner · SFAL K-Tech Centre of Excellence (Govt. of Karnataka) How this role works The internship is a working assessment, not a queue. If you're performing, we move you to full-time quickly — we're not going to make a good engineer wait out a fixed term. That's why relocation readiness matters from day one: full-time here means being in Bengaluru and on client deployments. What you'll work on

You'll work directly on our proprietary detection models — improving accuracy, shrinking them, and making them run faster on edge hardware.

Improve detection accuracy on industrial and logistics use cases: object detection, quality inspection, defect and anomaly detection

Train and evaluate models on the InSiSoNet pipeline; every change measured against before/after metrics

INT8 quantisation and quantisation-aware training — hold accuracy while cutting size and latency

ONNX export and compilation for edge accelerators

Analyse failure modes on real customer imagery and fix what's actually wrong, not what's easy to fix

Contribute to our internal annotation and training tooling

Document your work so another engineer can reproduce it without asking you What you need

Core





Strong grasp of object detection architectures — anchor-free heads, feature pyramids, loss design. You should be able to explain why an architecture is shaped the way it is, not just call the API.

PyTorch at depth — custom layers, custom training loops, custom loss functions

Model optimisation: quantisation (INT8, QAT), pruning, knowledge distillation

ONNX export and the debugging that comes with it

Ability to read a paper and implement it

You can look at a PR curve or a confusion matrix and say what's actually wrong with the model

Also expected

Python, strong. Git. Linux comfort.

Numerical intuition — you notice when a metric is too good to be true

Good to have

Training from scratch rather than fine-tuning pretrained weights

Inference runtimes: TensorRT, OpenVINO, TFLite, Hailo SDK

Efficient architecture design for constrained hardware

C++

Published work, Kaggle placings, or open-source contributions in CV Who this suits

Recent graduates in any computer science/AI or quantitative discipline. What matters is that you've trained a model end to end and can defend every choice in it — architecture, loss, augmentation, evaluation. Your branch matters less to us than whether you can explain why your model failed.

Working arrangement

Starts remote or on-site depending on your situation, but you must be willing to relocate to Bengaluru. Full-time conversion means being here.

Daily 60-minutes standup, camera on, fixed time

Short written update at end of each day

Travel for deployment and integration work; costs covered Duration - 3 months To apply

Send your CV plus a short note covering:

One thing you've built. What broke the first time, and how you fixed it.

Which role you're applying for.

Your availability — start date, relocation readiness.

📌 Deep Learning Engineering Intern — Edge Vision (India)
🏢 InSiSo Technolgies
📍 India