NHS Human Services, Inc.

APPLE INC has the following available for Machine Learning Research Engineer (REQ#200594881) in Seattle, Washington. Build and enhance features to improve discoverability of the content in Apple products, services and applications, including iTunes Store, App Store, Apple Music, Movies, Podcasts, and iBooks. Improve query understanding, classification, and ranking of search results for Apple applications and services. Work on feature engineering and model building for search ranking and query understanding to improve search quality and drive user engagement. Improve recall and precision of search results for Apple applications and services using a deep understanding of machine learning algorithms and information retrieval systems including decision trees, random forest, Deep Neural Networks, and Solr. Use big data technology and parallel processing technologies such as Map Reduce to baseline and prioritize content discovery features for better search recall and ranking. Ensure successful deployment of features and ranking models in production and A/B testing to optimize performance. Collaborate with other world-class engineers, researchers, and statisticians to ensure features and models are functioning at or above expected performance levels. Utilize knowledge of scalable data structures, object-oriented software design, and Unix to write code for state-of-the-art search to improve system quality. Bring the latest in search and discovery ideas and innovate beyond them at large scale production to advance our search capabilities. Define metrics that measure the success of machine learning models to drive meaningful improvements. $171,454 - $250,600/yr. REQUIREMENTS: 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: 1. 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. 2. 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. 3. 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. 4. Leveraging distributed systems for large-scale data processing, parallelized training, scalable storage, load balancing, and A/B testing demonstrates a scalable and resilient infrastructure. 5. 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. 6. Utilizing coding languages like Go, Python, Scala, and Spark for building pipelines, training models, and deploying them to serving stacks. 7. 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: 1. 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. 2. Incorporating a big data pipeline for processing logs and creating signals reflects a data-driven approach to continuously improving ranking and retrieval accuracy. APPLY: Submit resume online at https://jobs.apple.com/en-us/