Machine Learning Engineer (Recommendation), e-Commerce

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Machine Learning Engineer (Recommendation) – E-Commerce

Company Overview

This organization pioneers large-scale recommendation systems that serve diverse e-commerce scenarios, offering cutting-edge solutions across global markets. With a mission to innovate and optimize recommendation algorithms and strategies, they are committed to delivering superior e-commerce experiences.

Job Description

As a Machine Learning Engineer, contribute to the development and optimization of advanced recommendation systems, supporting diverse e-commerce offerings, including products, short videos, and live streams.

Key responsibilities include:

1. Designing recommendation models to address heterogeneous e-commerce goals across countries.
2. Leveraging deep learning, transfer learning, and multi-task learning to enhance recommendation models at scale.
3. Conducting data mining and analysis to improve the relevance and quality of recommendations.
4. Tackling challenges like diversity, content discovery, and cold-start problems to enrich user experience.
5. Developing innovative e-commerce algorithms and scalable AI/ML solutions.
6. Running experiments to test deployed models and addressing performance issues.
7. Collaborating with teams to deploy AI/ML solutions, focusing on data transformation and optimization.

Qualifications

Required:

1. Strong understanding of data structures, algorithms, and excellent programming skills.
2. Applied machine learning experience with algorithms like Collaborative Filtering, Word2Vec, Gradient Boosting Trees, and Deep Neural Networks.
3. Familiarity with recommendation system components (recall, sort, reranking, cold-start problems).
4. Proficiency in C++, Python, Big Data tools (Hive SQL/Spark/MapReduce), and deep learning tools (TensorFlow/PyTorch).
5. Excellent communication, teamwork, and a proactive approach to adopting new technologies.

Preferred:

1. Experience in personalized recommendations, online advertising, or information retrieval.
2. Publications in leading conferences such as NeurIPS, ICML, or RecSys.
3. Recognized achievements in data mining, machine learning, or programming competitions.
4. Demonstrated expertise through widely recognized machine learning projects.

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