Responsibilities:

• Collaborate closely with the data science and engineering teams to develop and enhance the job recommendation system on our platform.
• Design, implement, and iterate on machine learning models and algorithms that deliver personalized job recommendations to users based on their profiles, preferences, and historical interactions.
• Analyze large datasets containing user behavior, job listings, and course enrollments to extract valuable insights that inform the recommendation system's improvements.
• Develop and maintain data pipelines to preprocess, clean, and transform raw data into suitable formats for training and inference.
• Fine-tune and optimize recommendation models using techniques such as collaborative filtering, content-based filtering, and hybrid approaches.
• Experiment with novel approaches, algorithms, and features to continually enhance the recommendation system's accuracy and relevance.
• Collaborate with cross-functional teams to integrate the recommendation system seamlessly into the user interface and ensure smooth user experiences.
• Monitor and evaluate the performance of recommendation models, conducting A/B tests and implementing metrics to assess their effectiveness.
• Stay up-to-date with the latest advancements in machine learning and recommender systems, incorporating cutting-edge techniques into our solutions.
• Document research, development, and deployment processes to ensure knowledge sharing and maintainable codebase.

Qualifications:

• Bachelor's/Master's degree in Computer Science, Data Science, Machine Learning, or a related field.
• Strong programming skills in Python and proficiency in relevant libraries such as TensorFlow, PyTorch, scikit-learn, or similar.
• Solid understanding of machine learning concepts, algorithms, and best practices.
• Experience working with large datasets, data preprocessing, and feature engineering techniques.
• Familiarity with different recommendation algorithms, including collaborative filtering, matrix factorization, and neural networks for recommendation.
• Knowledge of natural language processing (NLP) and sentiment analysis for enhancing recommendation quality.
• Hands-on experience with data processing tools and frameworks (e.g., SQL, Apache Spark) is a plus.
• Excellent problem-solving skills and ability to translate business requirements into technical solutions.
• Strong communication skills to collaborate with cross-functional teams and present findings and ideas effectively.
• Experience with version control systems like Git and agile development practices.
• A portfolio or examples of past projects related to recommendation systems is highly desirable

About Company:
mUni Seagull is a leading online education platform committed to providing personalized learning experiences for students across the globe. Our mission is to connect learners with the right courses and job opportunities that match their skills and aspirations. We're seeking a talented Machine Learning Engineer to join our dynamic team and help us revolutionize how students discover and pursue their dream careers. 
Website: mUni Seagull