About the role#
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 will take ideas from experimentation to working prototypes while collaborating with our engineering, research, and product teams.
What you'll do#
- 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 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 with engineering and product teams to test new approaches, document findings, and improve recommendation systems.
- Build data processing and feature engineering pipelines.
- Experiment with recommendation architectures.
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.
- Familiarity with 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.
- Experience building academic or personal projects involving machine learning or recommendation systems.
- Familiarity with 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.
Location & details#
- This is a remote, full-time, paid internship.
- The term is rolling.
About Hook
Hook operates a social music platform that allows users to interact with and modify songs. The company provides creative tools for remixing music and sharing content within communities. It maintains partnerships with artists, labels, and rightsholders to manage licensing. Founded in 2023, the firm is based in New York and employs between 11 and 50 people.
How to get in at Hook
Applying early is a significant advantage when pursuing an internship at Hook. Because recruiters often review applications in the order they arrive, using Intern Insider to receive an instant alert the moment a role is published helps you join the applicant pool before it grows too large. This timing ensures your materials are among the first considered. You can also improve your response rate by reaching out to the team directly. Intern Insider surfaces the specific recruiters behind Hook roles, allowing you to send a professional inquiry or request a referral. Connecting with the right person often provides more visibility than submitting an application into a general queue.
