Computer Vision

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. 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. 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