Apple is seeking a Recommendation Systems Engineer to design, develop, and optimize large-scale recommendation and search systems for upcoming products, aiming to personalize user experiences and enhance human-computer interaction.
Requirements
- 10+ years of experience in Machine Learning, Data Science, or Software Engineering roles with a significant focus on recommendation systems and/or search infrastructure.
- Validated experience building and deploying large-scale Recommendation or Search systems in production.
- Strong proficiency in Python, Scala, or Java or other generalist programming languages
- Deep familiarity with ML frameworks (TensorFlow, PyTorch, XGBoost, etc.).
- Solid understanding of ML system design, model lifecycle, and experimentation pipelines.
- Extensive experience working with large datasets, data processing pipelines (e.g., Spark, Flink), and scalable architectures.
- Deep understanding of information retrieval, ranking algorithms, and user modeling techniques.
Responsibilities
- Designing and implementing recommendation algorithms including collaborative filtering, content-based filtering, deep learning models (e.g., DLRM, transformers), and hybrid systems.
- Developing personalized search and retrieval systems, optimizing ranking and relevance through ML/AI models and heuristics.
- Driving end-to-end machine learning workflows - from data ingestion and preprocessing to model training, deployment, and monitoring in production.
- Collaborating with cross-functional teams including Research Scientists, Product, Data Engineering, Search Infra teams, and UX to align recommendations/search features with business and user goals.
- Defining and implementing offline and online evaluation metrics, A/B testing frameworks, and continuous improvement strategies.
- Staying up to date with the latest research and innovations in recommendation systems and search-related ML technologies, and translating them into scalable production systems.
Other
- Ideal for a technically deep individual who thrives in a fast-paced environment, has a strong product sense, and enjoys solving real-world problems.
- BS or MS in Computer Science, Machine Learning, Statistics, or a related field.
- PhD Preferred
- Published work or patents in the domain of search/recommendation systems or related ML fields.
- Experience with modern vector search and retrieval techniques
