Machine Learning Engineer

Responsibilities: Develop, implement, and optimize core recommendation algorithms, including "collaborative filtering, content-based and combination of both". Implement real-time context awareness features such as detecting content surges (viral clips, trending topics) and making recommendations based on real-world events or seasonal/temporal factors. Apply multi-modal content understanding by utilizing computer vision for scene/key moment detection and NLP/speech analysis for commentary and tone. Conduct A/B testing of existing recommendation algorithms and analyze their performance. Personalize recommendations by leveraging user segmentation and contextual signals. Develop models to predict likelihood of content completion or dwell time. Ensure the maintenance and improvement of developed models and algorithms. Required Qualifications: A Bachelor's or Master's degree in Machine Learning, Computer Science, or a closely related quantitative field is highly preferred. Alternatively, candidates demonstrating substantial equivalent practical experience in this domain will also be considered. Strong programming skills in Python (preferred) or Scala. Experience with ML libraries such as TensorFlow, PyTorch, or scikit-learn. Basic knowledge of big data processing frameworks (e.g., Spark). Experience working with version control systems (Git) Desirable Qualifications: Experience developing real-time systems and high-performance applications. Knowledge of cloud platforms (AWS, GCP, Azure) and relevant ML services. Specific projects or experience in computer vision and natural language processing (NLP). Experience in the video streaming platforms or media industry. In-depth knowledge of A/B testing frameworks and experimental design.