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Syte

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About

Syte is a visual AI platform that helps connect shoppers with products they'll love and streamline merchandising. It offers solutions for visual discovery, AI tagging and merchandising, and hyper-personalization. Syte's technology enhances online product discoverability, improve CX and increasing revenue.

Platform
Web
Task
product discovery

Features

hyper-personalization

merchandising

image search

inspiration gallery

ai tagging

visual discovery

visual recommendation engines: shop similar, shop social, shop the look/room & 5 more

product discovery platform

Job Opportunities

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Syte

Mid-level Full Stack Developer

Syte is a visual AI platform connecting shoppers with products using visual search and AI-driven product tagging and merchandising.

engineeringonsiteTel Aviv, ILfull-time

Experience Requirements:

  • 3+ years of professional experience in full-stack development

  • 2+ years of working in Node.js

  • 2+ years of working with modern JavaScript frameworks such as React/Redux

Responsibilities:

  • Work with the latest technologies such as NodeJS, TypeScript, NestJs, Kafka, React, MongoDB, and more.

  • Plan, design & develop new features consumed by millions of users every day.

  • Participate in system design and code reviews.

  • Tackle tough problems and find creative ways to solve them.

  • Be in charge of different microservices, maintain and improve them Write beautiful code that is readable, testable, and extensible

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Python Developer

Syte is a visual AI platform connecting shoppers with products using visual search and AI-driven product tagging and merchandising.

Experience Requirements:

  • 4+ years of programming languages such as Python (preferably)

  • Experience with big data technologies (e.g., Spark, Kafka), cloud platforms (e.g., AWS, Azure), and containerized environments (Docker, Kubernetes)

  • Solid understanding of data warehousing, ETL processes, and data pipeline design

Responsibilities:

  • Design, implement, and optimize scalable data pipelines and architectures for large-scale data processing

  • Ensure data quality, integrity, and availability by establishing best practices and monitoring systems

  • Troubleshoot and resolve data-related issues to ensure continuous improvement

  • Drive innovation by evaluating new data technologies and tools to improve data engineering practices

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