Lead Real-Time Computer Vision Engineer

RIME | رايم; Riyadh; On-site; SAR 260,000 - 380,000; Full time

Job summary

Rime is seeking a Senior Computer Vision Engineer to lead the development, training, and deployment of real-time CV models that analyze complex environments. You will bridge AI research and production-grade engineering to ensure low-latency inference and scalable vision products.

You will work with PyTorch/TensorFlow, OpenCV, and modern architectures like YOLO, optimizing models with TensorRT/ONNX, and handling video/image datasets and labeling pipelines.

Qualifications

• Strong proficiency in PyTorch (or TensorFlow) and OpenCV.
• Experience optimizing CV models for real-time inference (TensorRT or ONNX).
• Solid coding skills in Python and a good working knowledge of C++.
• Understanding modern CV architectures (e.g., YOLO variants) and multi-object tracking.
• Experience with processing video/image datasets, data augmentation, and labeling pipelines.
• Bachelor’s degree in Computer Science, Artificial Intelligence, or related field; Master’s degree is a plus.

Responsibilities

• Lead development, training, and deployment of state-of-the-art CV models (detection and tracking).
• Oversee data pipelines and optimize models for low-latency real-time inference.
• Bridge AI research and production-grade engineering to scale smart vision products.

Skills

Python

C++

Real-time inference

Data handling

Education

Bachelor's degree in Computer Science / AI

Master's degree is a plus

Tools

PyTorch

TensorFlow

OpenCV

TensorRT

ONNX

YOLO variants

Job description

Rime is seeking a Senior Computer Vision Engineer to lead the development, training, and deployment of real-time CV models that analyze complex environments. You will bridge AI research and production-grade engineering to ensure low-latency inference and scalable vision products.

You will work with PyTorch/TensorFlow, OpenCV, and modern architectures like YOLO, optimizing models with TensorRT/ONNX, and handling video/image datasets and labeling pipelines.