About the job
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!
Responsibilities
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.
Qualifications
Minimum
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
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