
Senior Machine Learning Engineer II Full-Time Haifa, Israel
- חיפה
- משרה קבועה
- משרה מלאה
- Computer Vision & Image Intelligence
- Expertise in CNN based models as well as transformer-based vision models for analyzing, scoring, performing similarity vector analysis and tagging photos with metadata such as:
- Contextual categories (e.g., vacations, events, portraits)
- Aesthetic appeal and print-worthiness
- Area of interest
- Design and prototype custom, next-generation transformer architecture (beyond off-the-shelf models) tailored to Shutterfly product creation needs.
- 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.
- Lead experimentation frameworks (e.g., A/B testing) to optimize recommendation relevance and conversion.
- Architecture
- Define architectural direction for AI components and ensure their integration with core systems.
- 3+ years of experience in Deep Learning focused roles.
- Deep knowledge of computer vision techniques including CNNs, vision transformers, object detection, image classification, and image embedding.
- Proven experience designing and developing custom deep learning models—not just applying off-the-shelf architectures—to solve domain-specific challenges.
- Proficiency in Python and relevant ML libraries (e.g., PyTorch, TensorFlow).
- 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).
- MSc in Computer Science or related field.
- Experience with recommendation systems, collaborative filtering, deep learning for personalization, as well as transformer-based models for recommendation systems.
- Hands on experience in diffusion models, graph neural networks (GNNs) or reinforcement learning.