Data Science Intern (Personalization & Recommender Systems)

📋 Description

• Design and deploy state-of-the-art recommender systems for ranking and discovery
• Develop user/item representations with embeddings, sequence/graph methods
• Build systems leveraging real-time signals for dynamic personalization
• Apply exploration–exploitation techniques (contextual bandits, RL)
• Improve diversification, novelty, and long-term user engagement
• Run large-scale A/B experiments to evaluate models in production

🎯 Requirements

• Currently pursuing or recently completed a Master's or PhD in CS, ML, or statistics
• Proficiency in Python and modern ML stack (PyTorch, TensorFlow, Pandas, SQL)
• Strong ML/statistics foundations
• Publications or submissions in top venues (KDD, RecSys, ICML, NeurIPS)
• Experience with recommender systems (CF, deep recommenders, ranking)
• Representation learning / embeddings

🎁 Benefits

• Paid internship with potential for extension or return offer
• Hybrid in-office schedule: 3 days/week in SF; remote up to 4 weeks/year
• Mentorship from ML engineers and applied scientists
• Exposure to real-world data and production systems
• EEO and accommodations available during recruitment