Recommendation Systems Intern

Recommendations Systems Intern
Location: Remote 
Internship Type: Part-time, 3–6 months
Team: Recommendations

🌐 About Us
www.fAIshion.AI - We’re building an AI Stylist that lets anyone try on outfits, create full looks, and generate editorial-quality photos and videos — with themselves as the model. This goes beyond virtual try-on. We’re enabling personal style expression and creator-level content, not just fit checks. We already have users in 100+ countries, using our try-on across 1,000+ fashion sites. Now we’re expanding into a mobile-first styling and creator platform, where users can mix pieces, plan outfits for real events, and share fully shoppable looks.

Role Overview

We are seeking a Recommendation Systems Intern who is eager to learn how modern recommendation engines are built, understand user behavior through data, and collaborate closely with product managers, data analysts, and engineers. This unpaid internship is structured for educational and professional development, offering guided, hands‑on exposure to algorithms, modeling techniques, and real‑world system design.

What You’ll Learn & Practice

• Recommendation System Development: Participate in the development and optimization of core modules such as recall and ranking.
• Collaborative Filtering: Apply user‑based and item‑based collaborative filtering while learning similarity methods like Cosine Similarity, Jaccard Similarity, and Pearson Correlation Coefficient.
• Matrix Factorization: Practice SVD and related techniques to understand sparse matrices and latent feature extraction.
• Embedding Models: Explore deep‑learning‑based embedding technologies to map users and items into vector spaces.
• Multi‑Modal Modeling: Research and experiment with models like CLIP to understand product content (images, text) and address cold‑start and diversity challenges.
• User Behavior Analysis: Conduct supervised analysis of user behavior data to uncover patterns and propose model improvements.
• Cross‑Functional Collaboration: Work with backend engineers to understand data flow, API design, and system integration.
• Model Evaluation: Contribute to A/B testing and learn how to measure recommendation effectiveness.
• Documentation & Communication: Document code logic, expected behaviors, and model assumptions for testing and knowledge sharing.
• Task Management: Use JIRA to manage tasks, track bugs, and collaborate across teams.

Essential Skills & Qualifications

• Strong foundations in recommendation system algorithms
• Understanding of collaborative filtering and matrix factorization
• Familiarity with similarity metrics and their use cases
• Knowledge of SVD and its role in recommendation systems
• Understanding of embedding concepts and representation learning
• Interest in multi‑modal models such as CLIP
• Proficiency in Python and familiarity with ML/DL frameworks (TensorFlow, PyTorch)
• Strong data analysis skills and problem‑solving mindset
• Detail‑oriented, self‑driven, and eager to learn

We Value Candidates Who

• Actively use AI tools (ChatGPT, Claude, Cursor, Copilot, etc.) in their daily workflow
• Can clearly explain how AI improves their productivity and decision‑making
• Demonstrate AI‑native thinking — knowing when to prompt, automate, delegate, or prototype with AI
• Have experience “using AI at scale,” such as automating workflows, analyzing large datasets, or building AI‑assisted prototypes

Bonus Points

• Prior experience with recommendation system projects
• Participation in Kaggle or similar competitions
• Familiarity with big‑data technologies (e.g., Spark) or online serving frameworks
• Interest in fashion, e‑commerce, or AI‑driven products
• Public portfolio, GitHub, or personal ML projects

What You’ll Gain

• 🎓 Academic credit or recommendation letter (if eligible)
• 🎧 Real portfolio projects demonstrating applied machine learning
• 💡 Direct mentorship from Silicon Valley mentors
• 🎯 Proven track record: 6 of our past interns have received full‑time offers from top tech companies
• 🚀 Career‑ready experience in recommendation systems, data analysis, and applied AI

📌 To be considered, please complete this form in addition to Handshake:
👉 fAIshion.AI Interest Form

We’ll reach out via email after reviewing.