
Principal AI Engineer Full-Time Remote, United States
- USA
- Permanent
- Full-time
- Computer Vision & Image Intelligence
- Expertise in any CNN based models (MobileNetV3 VGG, Inception) as well as transformer-based vision models (e.g., ViT, CLIP, BLIP, DINOv2) for analyzing, scoring, performing similarity vector analysis and tagging photos with metadata such as:
- Facial expression, recognition and grouping
- Contextual categories (e.g., vacations, events, portraits)
- Aesthetic appeal and print-worthiness
- Facial recognition and grouping
- Object and scene detection
- Develop scalable pipelines for processing large volumes of image data and generating structured, query-able metadata.
- Integrate image intelligence with downstream applications such as search, ranking, and personalization.
- Behavioral AI & Recommendation Systems
- Build and deploy transformer-based ML models that analyze historical user behavior and purchasing patterns to generate personalized product recommendations.
- Correlate behavior data with metadata from image scoring models to curate visual content for individual users.
- Lead experimentation frameworks (e.g., A/B testing) to optimize recommendation relevance and conversion.
- Leadership & Architecture
- Define architectural direction for AI components and ensure their integration with core systems.
- Provide hands-on technical leadership, mentorship, and code contributions across teams.
- Drive strategic decisions regarding data acquisition, model training, deployment, and monitoring.
- 10+ years of experience in software engineering with at least 5+ years in AI/ML-focused roles.
- Deep knowledge of computer vision techniques including CNNs, vision transformers, facial recognition, object detection, image classification, and image embedding.
- Strong experience with recommendation systems, collaborative filtering, deep learning for personalization, as well as transformer-based models for recommendation systems.
- Proficiency in Python and relevant ML libraries (e.g., PyTorch, TensorFlow, OpenCV,
- Experience deploying ML models at scale (e.g., using Docker, AWS/GCP, or similar).
- Proven ability to lead cross-functional technical initiatives from ideation to delivery.
- Strong grasp of ML lifecycle best practices (data management, model evaluation, explainability, observability).
- MS in Computer Science or related field.