Data Scientist – Computer Vision & Machine Learning

Key Responsibilities

Advanced Computer Vision Development:

• Object Detection & Localization: Design, build, and deploy models using cutting-edge techniques like YOLO for object detection and localization.
• Image & Video Captioning/Tagging: Develop systems for generating descriptive captions and tags using models like RAM and Florence 2 .
• Embedding Techniques: Implement techniques for image and video embeddings to support tasks like similarity search and recommendation systems .
• Asset Fingerprinting (Bonus): Research and develop fingerprinting techniques for uniquely identifying and tracking visual content.

Data Modeling & Semantic Analysis:

• Build multi-modal data models to integrate and semantically understand visual data.
• Develop components that support semantic similarity and search/retrieval tasks based on visual inputs.

Engineering & Deployment:

• API Development: Create robust APIs and scalable solutions using FastAPI , Docker , and Azure .
• Deployment: Lead projects from proof-of-concept to production-ready deployment .

Green Field Innovation:

• Innovate in areas with vague or open-ended specifications , turning abstract ideas into concrete, scalable solutions.

Software Design Patterns:

• Implement and advocate for standard design patterns (e.g., ports and adapters , provider patterns ) to ensure system scalability and reliability.

Data Source Integration:

• Interface with various data sources to build comprehensive solutions tailored for visual data.

Required Skills & Experience
Expertise in Computer Vision:

• Proven experience in object detection , localization , image/video captioning , tagging , and embedding .
• Familiarity with state-of-the-art models such as YOLO for object detection and frameworks like RAM and Florence 2 for captioning and tagging.
• Expertise in asset decomposition -extracting structured and semi-structured representations for downstream tasks (e.g., vector search , querying).
• (Bonus) Experience in asset fingerprinting techniques.
• Proficiency in popular computer vision frameworks like OpenCV , TensorFlow , and PyTorch .

Bonus Skills
Graph Machine Learning:

• Experience with Graph Neural Networks (GNNs) and other graph-based machine learning techniques .

Agentic Applications:

• Familiarity with frameworks for agentic applications like Crew , Autogen , and Pydantic AI .

Role Expectations

This is a leadership role ideal for someone eager to push the boundaries of computer vision , innovate across multiple projects, and drive initiatives from conception to deployment. If you are a visionary in the field of computer vision and machine learning , this position offers you the opportunity to lead cutting-edge projects in a fast-paced, innovative environment.

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