Computer Vision

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