AIML - Machine Learning Engineer - Answers, Knowledge & Information (AKI) at Apple

Santa Clara, California, United States -

Full Time

Start Date

Immediate

Expiry Date

13 Feb, 26

Salary

0.0

Posted On

15 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Software Engineering, Python, C/C++, Data Generation, Data Quality Assessment, Search Technologies, NLP, Recommendation Systems, Information Retrieval, Metrics Development, Statistical Modeling, Collaboration, Critical Analysis, Quality Metrics, Technical Vision

Industry

Computers and Electronics Manufacturing

Description

The Answers, Knowledge and Information team is revolutionizing the way hundreds of millions of people access information on their devices, all while keeping user privacy at the forefront. As an Applied ML team, we're pushing the boundaries of Apple Intelligence, result ranking, and innovative search technologies, all while running a low latency production service. Our work fuels intuitive information experiences across some of Apple's most iconic products, including Siri, Spotlight, Safari, Messages, Lookup, and more. Join us in shaping the future of how the world connects with information! We are looking for a senior Machine Learning Engineer with a passion for building scalable, high quality solutions to evaluate personalized search applications. In this role, will directly impact how we assess and improve the quality of novel search applications by driving solutions for data generation, data quality assessment, offline and online metrics, and methods for statistical model and search quality assessment. Your work will impact the quality of innovative Search experiences across Apple products. DESCRIPTION In this role you will have the opportunity to develop data generation methodologies for personalized model and search systems evaluations, online and offline metrics for quality assessment and build scalable systems for comprehensive evaluation. Our team is responsible for models and evaluation of search experiences that answer user’s questions using their personal documents with privacy at the forefront. Role responsibilities include: - Design and implement novel data generation and data quality assessment methods to support modeling and evaluation of personal Q&A. - Build scalable, automated systems for large scale, end-to-end evaluation of models and search powered systems. - Design and implement offline and online metrics to assess product and component level quality for personal Q&A. - Collaborate with partner teams to define data and evaluation requirements and priorities, and to explore opportunities for enhancements to the Personal Q&A stack. - Develop long-term technical vision for Personal Q&A quality; identify problem areas and drive solutions as part of a larger roadmap. MINIMUM QUALIFICATIONS 6+ years industry experience in building Machine Learning or ML evaluation systems at scale Strong software engineering skills in mainstream programming languages, such as: Python, C/C++ Strong communication skills and ability to drive solutions in collaboration with partner teams Bachelors in Computer Science, Engineering, Statistics or related field PREFERRED QUALIFICATIONS Experience in design and building production ML systems and applications in search, NLP, recommendation systems, or information retrieval Experience in data collection, data generation and/or data quality assessment of language, image or multi-modal data Ability to quickly prototype ideas and solutions, and perform critical analysis Strong skills for quality metrics development; interpretation of evaluations; and presentation to executive audience Advanced degree (Master’s or Ph.D.) in Computer Science, Engineering, Statistics, or related field, or equivalent industry work experience

Responsibilities

Develop data generation methodologies for personalized model and search systems evaluations and build scalable systems for comprehensive evaluation. Collaborate with partner teams to define data and evaluation requirements and explore opportunities for enhancements to the Personal Q&A stack.