About Apple
Apple · Cupertino
Senior ML Engineer - Search & Recommendation Systems
(f/m/d) · 5/15/2025
Description
This role is ideal for a technically deep individual who thrives in a fast-paced environment, has a strong product sense, and enjoys solving real-world problems using modern and novel AI models and scalable systems. We are passionate team of hardworking engineers, researchers, and scientists with common goals in mind, and we are looking for a strong Search and Recommendations Machine Learning engineer to join our team and be a part of the next revolution in human-computer interaction!
KEY RESPONSIBILITIES INCLUDE BUT ARE NOT LIMITED TO:
- 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.
Qualifications
- 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.
- Experience with real-time systems, user feedback loops, and model retraining pipelines.
- BS or MS in Computer Science, Machine Learning, Statistics, or a related field.
Preferred Qualifications
- 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
- Strong foundation in deep learning architectures for personalization (e.g., transformers, graph neural networks, multi-task learning).
- Exposure to multi-objective optimization in recommender systems (e.g., engagement, diversity, novelty, fairness).
- Familiarity with MLOps tools and cloud platforms (AWS/GCP/Azure, Kubeflow, MLFlow, etc.).
Benefits
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