Company Description:

McEasy, a transportation management solution to simplify complex logistics operations. is looking for an Computer Vision Engineer to join our ever-growing team.

If you are a keen learner, self-driven, and looking to be a part of a team that is passionate with helping each other, we want to hear from you.

Job Description:

1. Own Video Intelligence

Build and train CV models for driver fatigue & distraction detection, ADAS-style road & event detection, and cargo, theft, and in-cabin monitoring.
Turn messy, real-world video into reliable detections.

2. Optimize for the Edge

Make models run cost-effectively at scale using quantization, pruning, distillation, on-device/edge inference, and trigger-based, event-driven processing.
Treat inference cost-per-camera as a first-class design constraint.

3. Train, Don't Just Wrap

Build custom models where they create differentiation.
Use pre-trained backbones and transfer learning to move fast.
Know when to fine-tune vs. build from scratch.

4. Own the Vision Data Pipeline

Define annotation specs and quality standards (labeling is outsourced — you own the spec).
Build training and evaluation datasets from real fleet video.
Monitor model drift and retrain as conditions change.

5. Ship to Production

Deploy models into the product, not notebooks.
Build inference services (edge + cloud), monitoring, and versioning.
Iterate from real field performance.

6. Collaborate Across Teams

Work with Hardware/IoT Engineers on dashcams and edge devices.
Partner with Data & AI Product Engineers for shared data and benchmarking.
Collaborate with Software Engineers and Product/Leadership to integrate solutions and refine use cases.

Qualifications:

Must-Have

Strong computer-vision and deep-learning fundamentals (object detection, image/video models)
Hands-on with PyTorch or TensorFlow — training, not just inference
Track record deploying CV models to production (real users, real data — not just papers or Kaggle)
Experience optimizing models for real-time / resource-constrained inference
Solid engineering (Python; can build and ship services)
Comfort with messy, real-world image/video data at scale

Nice-to-Have

Edge / embedded deployment (NVIDIA Jetson, mobile, on-device, TensorRT/ONNX)
Driver monitoring / ADAS / dashcam / automotive vision experience
Data-centric ML and annotation-pipeline design
Inference cost optimization at fleet scale
MLOps: model versioning, monitoring, automated retraining