Job Description: Video Knowledge Engineer

Are you passionate about Computer Vision, Gen AI, and ready to dive into a thrilling project that blends cutting-edge technology with the vibrant world of entertainment?
Were seeking a skilled Computer Vision Knowledge Engineer to participate in building an AI platform that revolutionizes how we create, curate, and deliver short-form content. You'll work alongside product, engineering, AI, and machine learning experts to build real-world AI systems that shape the future of entertainment.
This role is ideal for someone who thrives at the intersection of Gen AI, machine learning, and software engineering, with a passion for building innovative AI products.

Responsibilities:

• Coordinate with internal teams and vendors to identify and implement optimal metadata and video knowledge extraction solutions for the AI platform, enabling effective creation, curation, and delivery of short-form content.
• Collaborate with internal stakeholders and external vendors to evaluate, utilize, and enhance computer vision tools and libraries, ensuring robust video content analysis and metadata extraction.
• Work closely with the team, partners and vendors to design and implement computer vision algorithms that extract actionable insights and high-quality metadata from video content.
• Partner with DevOps and engineering teams to integrate computer vision models and metadata extraction solutions into scalable, containerized environments using Docker and Kubernetes.
• Engage with data scientists, machine learning engineers, DevOps, and vendors to deliver cohesive, scalable solutions for video knowledge extraction and metadata optimization.

Qualifications/Requirements:

• Proficiency in Python.
• Solid experience with development and deployment tools, including containerization (Docker, Kubernetes) and workflow orchestration (Temporal or similar).
• Familiarity with video analysis tools and libraries such as OpenCV, FFmpeg, or similar.
• Experience in computer vision algorithms, preferably with broad familiarity and use of existing tools and libraries (e.g., OpenCV, PySceneDetect) for building CV applications.
• Expertise in multiple areas listed below:

• Object and people detection (must)
• Facial recognition (must)
• Image and video segmentation
• Activity recognition
• Video summarization
• Video identification
• Video behavior analysis

Preferred Qualifications:

• Practical knowledge of artificial neural networks for computer vision, especially in Convolutional Neural Networks (CNNs).
• Exposure to deep learning frameworks such as TensorFlow or PyTorch.
• Expertise in any additional CV areas listed below:

• Edge detection
• Style detection
• Gesture recognition
• Video tracking
• Object co-segmentation