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.