About the Job:
SCAI | سكاي is a leader in the AI industry, wholly owned by the Public Investment Fund (PIF). The company supports national priorities and innovations within the technology sector, aiming to position Saudi Arabia as a globally competitive hub for advanced technologies. We provide innovative and impactful AI and emerging technology solutions at scale, helping our partners solve complex challenges, accelerate growth, and drive business outcomes.
Location: Riyadh, Saudi Arabia (On-site)
Work Conditions: Full-time
Responsibilities:
- Design, implement, and optimize Computer Vision algorithms for real-time video analysis, object detection, tracking, and recognition to address urban challenges including traffic flow management, public safety, and infrastructure monitoring.
- Collaborate with engineers and data scientists to integrate computer vision models into larger smart city systems while ensuring alignment with product and operational goals.
- Build pipelines for processing and annotating large-scale video, image, and sensor data from urban environments, ensuring high-quality, scalable data handling.
- Optimize computer vision models for deployment in large-scale systems with millions of data points and real-time requirements, including both cloud and edge deployments.
- Continuously evaluate and improve the accuracy and performance of models in dynamic urban environments, implementing real-time monitoring and retraining systems to adapt to new data patterns.
- Maintain detailed documentation on algorithm development, data processing workflows, and deployment procedures, while regularly reporting on project progress and impact.
Job Requirements:
- Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Mathematics, or a related field.
- 3+ years of experience in computer vision and machine learning, preferably in applications such as surveillance or traffic management.
- Hands-on experience with deploying computer vision systems at scale, including edge computing and real-time data processing.
- Solid understanding of techniques such as object detection (e.g., YOLO, Faster R-CNN), image segmentation, tracking, and pose estimation.
- Proven experience with deep learning frameworks like TensorFlow, PyTorch, and Keras, and computer vision libraries such as OpenCV or Dlib.
- Experience with large-scale data processing and handling video and image data, including time-series data from urban sensors.
- Proficiency in Python and associated libraries (e.g., NumPy, Pandas) and C++ for performance-critical components.
- Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud) and edge computing for real-time model deployment.
- Experience in optimizing models for speed and scalability, especially for real-time applications.
