Machine Learning Engineering Intern, Recommendation Systems

Responsibilities

• Assist in implementing and experimenting with ML models for personalized recommendations, discovery and ranking
• Process datasets, extract features and prepare data for training and evaluation
• Train, benchmark and evaluate recommendation approaches to improve performance
• Translate ideas into working prototypes using Python and PyTorch or scikit-learn
• Work with engineering and product teams, document findings and contribute to recommendation systems

Requirements

• Currently pursuing a Bachelor's, Master's or PhD in CS, ML, Data Science, Statistics or related field
• Strong Python programming skills, with NumPy and pandas
• Foundational knowledge of machine learning, statistics and data analysis
• Hands-on experience with at least one ML framework, preferably PyTorch
• Interest in recommendation systems, retrieval, ranking or personalization
• Strong problem-solving skills and willingness to learn and collaborate

Preferred Qualifications

• Academic or personal projects involving ML or recommendation systems
• Collaborative filtering, content-based filtering or embeddings
• SQL, real-world datasets or user interaction data
• Deep learning frameworks, model evaluation or experimentation
• Personal interest in music, music discovery or social platforms

Benefits

• Hands-on experience with real-world recommendation and personalization systems
• Mentorship from experienced engineering, research and product teammates
• Freedom to experiment with new approaches, technologies and ML architectures

About the Company

Hook is a backed social music platform building music discovery, fan expression and creator growth through remix-friendly experiences with artist and rightsholder partnerships.