Computer Vision at PT Otto Menara Globalindo

Surabaya, East Java, Indonesia -

Full Time

Start Date

Immediate

Expiry Date

15 Sep, 26

Salary

0.0

Posted On

17 Jun, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Computer Vision, Deep Learning, PyTorch, TensorFlow, Python, Object Detection, Model Optimization, Edge Computing, MLOps, Data Pipeline Design, Inference Services, Transfer Learning

Industry

technology;Information and Internet

Description

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 Department: Engineering

Responsibilities

Develop and deploy computer vision models for driver monitoring and road event detection. Optimize these models for edge devices and manage the end-to-end vision data pipeline from annotation to production.