Apple: Maps Search and Machine Learning Engineer/Scientist
Seeking a Machine Learning Engineer/Scientist for contributing towards modeling and iteratively improving Apple Maps Search which powers Maps Autocomplete, Safari, Spotlight and others.
Location: Cupertino, CA
Position: Maps Search and Machine Learning Engineer/Scientist
Would you like to be part of a team that impacts millions of users every single day? Does finding patterns in data and building highly scalable data-driven systems to solve real-world problems excite you? Does designing and improving Local Search for all Apple Maps users appeal to you? If yes, we invite you to join our mission in building and redefining Apple’s Local Search. Apple is critically invested in the success of its mobile ecosystem. Maps and Local Search is a core asset that is at the crux of this ecosystem. Our team operates at the intersection of building highly scalable applications, analytics to understand user behavior and big data machine learning to improve our end-user experience.
- Expertise and experience in various facets of machine learning and natural language processing, such as classification, feature engineering, information extraction, clustering, semi-supervised learning, topic modeling and ranking
- Practical understanding of the mathematics behind modern machine learning, linear algebra and statistics.
- Strong programming and debugging skills in: Java or C/C++ or Python or equivalent
- Good knowledge of big data processing, prior experience with Hadoop, Spark, Hive, Pig is highly desired.
- Knowledge and prior experience with some deep learning frameworks is desired by not required.
- Excellent interpersonal and communication skills - working independently and/or in small teams
- Attention to detail, data accuracy and quality of output.
The goal of Maps Search team is to improve the local search experience of Apple device users. We believe local search is the core user experience on mobile platforms and we are committed to build the best possible product. At this role, you will be contributing towards modeling and iteratively improving Apple Maps Search which powers Maps Autocomplete, Safari, Spotlight and others. You will be tackling a variety of big data challenges in query understanding, ranking, information retrieval and information extraction - building, architecting and managing core machine learning models/algorithms to improve Apple’s Maps Search.
Job Responsibilities Include (not limited to):
- Apply knowledge of data mining, information retrieval, NLP and machine learning to develop key iOS and OS X features focused on improving local search experience.
- Collaborate with various teams (e.g., infrastructure, quality, data) to develop exciting features and contribute towards our mission of best search experience to our end-users.
- Design and build highly scalable, big data pipelines that enable enriching maps index with extremely relevant location-sensitive-knowledge.
- Design and develop tools, processes and analytics for backend data-extraction pipelines - along with performing hands-on analyses to answer hard hypotheses/questions.
- Own and conduct A/B tests for exploring various ideas and perform data analysis to infer insights from experiments to help ship features. Develop and mentor aspiring applied scientists / engineers to expand their scope and have a big impact. Be part of building a world class search team!
- Bachelor/Masters in Computer Science, Engineering or equivalent required.
- Experience/knowledge of large scale machine learning applied to very large and diverse datasets (preferred)
- Experience shipping products, especially ones incorporating machine learning (preferred)
- Experience/knowledge of search technology and related fields (preferred)
Apple is an Equal Opportunity Employer that is committed to inclusion and diversity. We also take affirmative action to offer employment and advancement opportunities to all applicants, including minorities, women, protected veterans, and individuals with disabilities. Apple will not discriminate or retaliate against applicants who inquire about, disclose, or discuss their compensation or that of other applicants.