Machine Learning Engineering Intern, Recommendation Systems

Hook is a social music platform, backed by top-tier investors, building the next generation of music discovery, fan expression, and creator growth. We turn the songs people love into sounds they can play with, enabling fans and creators to reimagine music through intuitive, remix-friendly experiences with zero learning curve.

By partnering directly with artists, labels, and rightsholders, Hook ensures music is discovered, shared, and expressed in ways that respect and reward the people behind it. At Hook, music isn't just consumed. It's experienced, expressed, and made personal.

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.

• 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.

• Hands-on experience building and improving real-world recommendation and personalization systems.

• The opportunity to work on the full ML development lifecycle, from data processing and model development to evaluation and product integration.

• Exposure to practical engineering challenges involving recommendation algorithms, ranking, retrieval, and personalization.

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

• Mentorship and opportunities to develop strong machine learning and software engineering skills.

• The freedom to experiment with new approaches, technologies, and ML architectures.

• The opportunity to make a direct impact on how millions of people could discover, interact with, and experience music.