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Apple Machine Learning Research Engineer United States, Washington, Seattle 186126101
07.04.2025
- Bachelor's degree or foreign equivalent in Computer Engineering, Computer Science or related field and 5 years of progressive, post-baccalaureate experience in the job offered or related occupation.
- 5 years of experience with each of the following skills is required:
- Leveraging data structures like List, Map, Hash tables, trie, and algorithms such as binary search, sorting, tf-idf, ndcg, and learning to rank showcases a rich toolkit for handling different aspects of data manipulation, model training, and evaluation.
- Incorporating information retrieval techniques like BM25, posting lists, semantic retrieval, embeddings, recall, demonstrates a thorough understanding of the intricacies involved in retrieving relevant documents from a large corpus.
- Employing a variety of machine learning models including SVMRank, XGBoost, Neural networks, and collaborative filtering ensures a diverse set of approaches to rank documents and provide a better user experience.
- Leveraging distributed systems for large-scale data processing, parallelized training, scalable storage, load balancing, and A/B testing demonstrates a scalable and resilient infrastructure.
- Applying neural network methods to generate embeddings for documents and queries signifies a commitment to semantic retrieval, acknowledging that understanding the context is as important as textual matching.
- Utilizing coding languages like Go, Python, Scala, and Spark for building pipelines, training models, and deploying them to serving stacks.
- Leveraging public cloud services like Cloudera, AWS for storage and data manipulation showcases a scalable and cost-effective solution for managing large datasets.
- 3 years of experience with each of the following skills is required:
- Utilizing Natural Language Processing for query understanding, entity recognition, genre classification, and query intent classification reflects a commitment to enhancing the search system's ability to comprehend user queries and improve relevance.
- Incorporating a big data pipeline for processing logs and creating signals reflects a data-driven approach to continuously improving ranking and retrieval accuracy.
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