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