Machine Learning Engineering Intern, Recommendation Systems

Job not on LinkedIn

🔥 0 minutes ago

🇺🇸 United States – Remote

👨‍🎓 Internship

⚪️ Entry-level

🤖 Machine Learning Engineer

🚫👨‍🎓 No degree required

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👻 Ghost score 12%

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Hook

WebsiteLinkedInAll Job Openings

11 - 50 employees

📱 Media

👥 B2C

💰 $3M Seed Round - Hook on 2025-03

Media • B2C

Hook is a mobile-first social music discovery app from Hook Music Inc. It lets users browse and remix short music “hooks,” participate in challenges and leaderboards, and engage with creator-driven audio content and filters. The platform is consumer-focused, emphasizing discovery, social sharing, and user-generated remixes and collaborations.

📋 Description

• 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

• 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

• 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

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