Machine Learning Engineering Intern, Recommendation Systems

We are looking for a Machine Learning Engineering Intern to help design, build, and improve the recommendation systems that power personalized music discovery and social experiences at Hook.

You will work on the engineering and applied machine learning challenges behind large-scale personalization, from processing user interaction data and building recommendation models to developing ranking algorithms and evaluating their performance.

You'll have the opportunity to implement ML algorithms, build data processing and feature engineering pipelines, experiment with different recommendation architectures, and contribute to the systems that help users discover music, engage with content, and connect with creators.

Working closely with our engineering, research, and product teams, you'll take ideas from experimentation to working prototypes, gaining hands-on experience building real-world machine learning systems in a fast-moving startup environment.

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

• 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.