Machine Learning Engineering Intern, Recommendation Systems

This is a fully remote position, open to applicants in United States.

📋 Description

• Support the implementation and experimentation of machine learning models focused on personalized recommendations, content discovery, and ranking.

• Process datasets, extract pertinent features, and prepare data for the training and evaluation of models.

• Train, benchmark, and assess various recommendation strategies to pinpoint areas for enhancing model performance.

• Convert concepts and algorithms into functional prototypes utilizing Python and frameworks such as PyTorch or scikit-learn.

• Collaborate closely with engineering and product teams to test novel approaches, document insights, and contribute to the advancement of Hook's recommendation systems.

• Assist in designing, constructing, and refining recommendation systems that drive personalized music discovery and social interactions.

• Tackle large-scale personalization challenges, which include processing user interaction data, developing recommendation models, executing ranking algorithms, and conducting performance evaluations.

⛳️ Requirements

• Currently enrolled in a Bachelor's, Master's, or PhD program in Computer Science, Machine Learning, Data Science, Statistics, or a related discipline.

• Proficient programming skills in Python along with familiarity with libraries such as NumPy and pandas.

• Basic understanding of machine learning, statistics, and data analysis.

• Practical experience with at least one ML framework, preferably PyTorch.

• Keen interest in recommendation systems, information retrieval, ranking, or personalization.

• Experience in developing academic or personal projects that involve machine learning or recommendation systems.

• Knowledge of recommendation techniques including 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.

• Personal passion for music, music discovery, or social platforms.

🏝️ Benefits

• Practical experience in developing and enhancing real-world recommendation and personalization systems.

• Opportunity to engage in the entire ML development lifecycle, from data processing and model creation to evaluation and integration into products.

• Exposure to real engineering challenges related to recommendation algorithms, ranking, retrieval, and personalization.

• Close collaboration with seasoned teammates across engineering, research, and product teams.

• Mentorship and opportunities to strengthen machine learning and software engineering skills.

• Freedom to explore new approaches, technologies, and ML architectures.

• Chance to have a direct impact on how millions of individuals discover, interact with, and enjoy music.