Machine Learning Engineer - Recommendation Systems

We are looking for a Machine Learning Engineer to design, build, and scale personalized recommendation systems that power discovery, ranking, and user engagement across our products.

Requirements

• Design and develop recommendation systems including Collaborative Filtering (user-item, item-item), Content-based and hybrid recommenders, Ranking and re-ranking models, Embedding-based retrieval (ANN, vector search)
• Train, evaluate, and iterate on models using offline metrics (NDCG, MAP, Recall@K) and online A/B experiments
• Build pipelines for feature engineering, model training, inference, and retraining
• Deploy ML models in production environments with low-latency constraints
• Optimize inference for scale (caching, batching, approximate nearest neighbors)
• Build real-time and batch recommendation pipelines
• Monitor model performance, data drift, and system health
• Work with large-scale datasets (clicks, impressions, transactions)
• Define success metrics for recommendations (CTR, CVR, retention)
• Run and analyze A/B tests and iterate based on results
• Work closely with product, data, and backend teams to translate business problems into ML solutions
• Contribute to ML best practices, documentation, and system design

Benefits

• Health insurance
• Retirement plan
• Paid time off
• Stock options