Machine Learning Engineer

Design and Develop Personalization Algorithms: Develop and refine algorithms that drive personalized content recommendations for millions of users worldwide. Utilize advanced machine learning techniques such as collaborative filtering, related model and deep to improve the accuracy and relevance of recommendations. System Architecture and Implementation: Architect and implement scalable systems for real-time recommendation service and batch processing of large datasets. This involves building data pipelines using technologies like Apache Spark, Hive, and Kafka, and setting up low-latency personalization Java runtime services utilizing technologies such as Apache Cassandra, Redis, Lucene and TensorFlow. Prototyping and Innovation: Continuously explore new data sources and machine learning methods to refine recommendation models. Develop internal tools for prototyping and visualization of new algorithms. This involves full-stack web development utilizing frontend and backend languages such as JavaScript and Java. Experimentation and Testing: Design and conduct A/B tests to measure the impact of different personalization strategies on user engagement and satisfaction. Analyze experiment results and integrate successful features into production systems. Production Deployment and Maintenance: Deploy personalization runtime algorithms and offline data pipelines into production environments, ensuring robustness and scalability. Monitor system performance and health, performing regular maintenance and updates as needed. Cross-Functional Collaboration: Collaborate with product managers, researchers, and software developers to explore and evaluate new personalization strategies to integrate personalization into the broader product ecosystem. Provide expertise on machine learning and personalization to guide product development and strategic decisions.

• Design and develop personalization algorithms
• Develop and refine algorithms that drive personalized content recommendations
• Utilize advanced machine learning techniques
• Architect and implement scalable systems
• Develop internal tools for prototyping and visualization
• Experiment and test different personalization strategies
• Deploy personalization runtime algorithms and offline data pipelines
• Collaborate with product managers, researchers, and software developers
• Provide expertise on machine learning and personalization

• Master’s degree or foreign equivalent in Data Science, Computer Science or related field
• Utilizing backend programming languages such as Java, Python, and Scala
• Utilizing frontend programming languages such as JavaScript
• Utilizing scalable datastore such as Cassandra, Redis and Voldemort
• Utilizing TensorFlow to develop and deploy deep learning model
• Utilizing big data processing technology such as Spark, HDFS, Hive and Iceberg
• Utilizing Kafka to work on cross datacenter data sync and replications
• Utilizing PySpark and PyTorch for Machine Learning
• Utilizing Hibernate and Spring MVC frameworks to interact with SQL databases
• Utilizing knowledge of search engines like Elasticsearch or Apache Solr

• Comprehensive medical and dental coverage
• Retirement benefits
• Discounted products and services
• Education reimbursement
• Discretionary restricted stock unit awards
• Employee Stock Purchase Plan
• Discretionary bonuses or commission payments
• Relocation assistance

Apple is a place where extraordinary people gather to do their lives best work. Together we create products and experiences people once couldn’t have imagined, and now, can’t imagine living without. It’s the diversity of those people and their ideas that inspires the innovation that runs through everything we do.