AI Research Engineer (Recommendation System)

We are looking for a "AI Research Engineer" on behalf of our global business partner runs in Telecommunication Industry.

Requirements:

Essential technical requirements:

A. Basic computer science and programming languages

• Experienced in developing and deploying deep learning models, especially in the area of recommender system.
• Proficiency in Python programming language is mandatory, with hands-on experience in deep learning frameworks like TensorFlow, PyTorch, or Keras.
• Strong analytical and problem-solving skills to translate business requirements into technical solutions.
• Proactive and self-motivated mindset with a keen interest in staying updated with the latest advancements in deep learning and recommendation systems research.

B. Machine Learning

• Solid understanding of neural network theory, including concepts such as optimization, linear algebra, loss functions, etc.
• Familiarity with modern machine learning concepts and architectures.

C. Working efficiency

• Proficiency in version control systems like Gitlab or Github.
• Experience with Docker for building a simulation of the production environment.
• Demonstrated expertise in data preprocessing techniques, feature engineering, and data augmentation to enhance model performance.
• Experience with Apache Spark or Hadoop and SQL proficiency is advantageous.

Qualifications:

• Bachelor's degree or higher in Computer Science, Engineering, Mathematics, or a related field. Advanced degrees (Master's or Ph.D.) are preferred.
• Being published in reputable journals related to recommender systems (e.g., ACL, SIGIR) is a significant plus.
• Fluency in English, both written and spoken.
• Ability to work effectively in a multi-disciplinary and multi-cultural team environment.
• Experience with popular deep learning frameworks (TensorFlow, PyTorch) and ML & DL libraries.
• Publication records in journals or conferences related with RecSys, IR, NLP especially in the areas of ctr prediction, vector retrieval, semantic search and ranking.
• Experience in integrating deep models and ranking models for large-scale recommendation engines using these specialized techniques.
• Previous project experience in search or recommendation systems domain is a big plus.