Machine Learning Engineering Intern, Recommendation Systems

Hook

. Assist in implementing and experimenting with machine learning models for personalized recommendations, content discovery, and ranking .

Posted 11/11/2026internshipRemote • United StatesEntry LevelWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in machine learning model implementation and evaluation, particularly in recommendation systems. Proficient in Python programming and familiar with frameworks such as PyTorch and scikit-learn, with a strong foundation in data processing and analysis.

Highest-signal resume keywords

Python ProgrammingMachine Learning FrameworksRecommendation SystemsData ProcessingModel Evaluation

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Machine LearningData AnalysisStatisticsCollaborative FilteringContent-Based FilteringEmbeddingsSQLNumPyPandasDeep Learning

Tools & Technologies

PyTorchScikit-learn

Industry Keywords

PersonalizationContent DiscoveryRanking AlgorithmsUser Interaction DataMusic Discovery

Tech Stack

Tools & technologies

NumpyPandasPythonPyTorchScikit-LearnSQL

About the role

Key responsibilities & impact

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
Help design, build, and improve recommendation systems powering personalized music discovery and social experiences
Work on large-scale personalization challenges, including user interaction data processing, recommendation models, ranking algorithms, and performance evaluation

Requirements

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
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
Personal interest in music, music discovery, or social platforms

Benefits

Comp & perks

Hands-on experience building and improving real-world recommendation and personalization systems
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
Freedom to experiment with new approaches, technologies, and ML architectures
Opportunity to make a direct impact on how millions of people could discover, interact with, and experience music