ML Engineering Intern: Music Recommendation Systems

Hook Music; EE. UU.; A distancia; USD 28.000 - 41.000; A tiempo parcial

Ventajas ofrecidas por este puesto de trabajo

Hands-on ML experience

Mentorship

Cross-functional exposure

Music industry exposure

Descripción de la vacante

Hook is seeking a Machine Learning Engineering Intern to help design, build, and improve our music recommendation systems. You’ll work on large-scale personalization, processing user interactions and evaluating ranking models to enhance discovery and social experiences.

Join a fast-moving startup, collaborate with engineering, research and product teams, and gain hands-on experience across the full ML development lifecycle from data processing to product integration.

Formación

• Pursuing Bachelor's/Master's/PhD in CS, ML, DS, statistics, or related field.
• Strong Python skills with NumPy and pandas.
• Foundational ML knowledge and data analysis.
• Hands-on experience with ML frameworks, preferably PyTorch.
• Interest in recommender systems and personalization.
• Strong problem-solving and collaborative attitude.

Responsabilidades

• Develop and experiment with ML models for personalized recommendations.
• Build data pipelines to extract features and prepare data for training.
• Train, benchmark, and evaluate recommender approaches.
• Translate ideas into prototypes using Python and PyTorch or scikit-learn.
• Collaborate with engineering and product teams to test approaches and document findings.

Conocimientos

Python

NumPy

pandas

Machine Learning

Statistics

Data Analysis

Problem Solving

Educación

Bachelor's/Master's/PhD in CS/ML/DS

Herramientas

PyTorch

scikit-learn

SQL

Descripción del empleo

Hook is seeking a Machine Learning Engineering Intern to help design, build, and improve our music recommendation systems. You’ll work on large-scale personalization, processing user interactions and evaluating ranking models to enhance discovery and social experiences.

Join a fast-moving startup, collaborate with engineering, research and product teams, and gain hands-on experience across the full ML development lifecycle from data processing to product integration.