Machine Learning Engineering Intern, Recommendation Systems

Overview

Machine Learning Engineering Intern on Hook's recommendation systems team, helping design, build and improve the models and ranking algorithms that power personalized music discovery and social experiences.

What you'll do

• Assist in implementing and experimenting with machine learning models for personalized recommendations, content discovery, and ranking.
• Process datasets, extract relevant features, and prepare data for model training and evaluation.
• Train, benchmark, and evaluate different recommendation approaches to identify opportunities to improve model performance.
• Translate ideas and algorithms into working prototypes using Python and frameworks such as PyTorch or scikit-learn.
• Work closely with engineering and product teams to test new approaches, document findings, and contribute to improving Hook's recommendation systems.

What you'll need

• Currently pursuing a Bachelor's, Master's, or PhD degree in Computer Science, Machine Learning, Data Science, Statistics, or a related field.
• Strong programming skills in Python and familiarity with libraries such as 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, information retrieval, ranking, or personalization.
• Strong problem-solving skills, curiosity, and willingness to learn and collaborate in a fast-paced engineering environment.

Nice to have

• Experience building academic or personal projects involving machine learning or recommendation systems.
• Familiarity with recommendation techniques such as collaborative filtering, content-based filtering, or embeddings.
• Experience working with SQL, real-world datasets, or user interaction data.
• Familiarity with deep learning frameworks, model evaluation, or experimentation.
• A personal interest in music, music discovery, or social platforms.

Details

• Remote work.