Imagine what you could do here. At Apple, new ideas have a way of becoming phenomenal products, services, and customer experiences very quickly. You bring passion and dedication to your job and there’s no telling what you could accomplish. We provide the machine learning infrastructure, data products, and algorithms supporting some of Apple’s newest and fastest growing services. Going beyond accuracy we are driven to develop fair, explainable algorithms that preserve user privacy. Machine Learning solutions at Apple require rigorous data engineering; we always launch at scale.

DESCRIPTION

As a Machine Learning Engineer on our team, you will build and iteratively refine model pipelines that enable rich text experiences on Wallet products. You will conduct experiments and create prototypes for new approaches to improve the quality of our generative language models, translating customer needs into analytical solution. You will work hand in hand with software engineers, data scientists, and product partners. Finally, you will implement the building blocks and infrastructure that bring these innovations into our production pipelines, and contribute evaluate metrics for measuring forward progress. KEY RESPONSIBILITIES - Development and maintenance of modeling pipelines that are deployed to production and reach global scale - Definition of robust automated evaluation metrics to enable continuous model improvements - Failure analysis to understand shortcomings and model sensitivities - Curation and synthesis of representative training and evaluation data - Implementation of experiments and simulations to assess proposed model changes

MINIMUM QUALIFICATIONS

• MS or PhD in Computer Science or related field with at least 2 years of industry experience
• Strong Python programming skills, with experience developing production-quality Python modules
• Solid background in machine learning, data science, natural language processing, or statistics

PREFERRED QUALIFICATIONS

• Experience building and maintaining model pipelines end-to-end, from data curation to evaluation
• Ability to design and implement experiments that bring NLP research ideas into production
• Familiarity with LLMs, RHLF, prompt engineering, data synthesis, automatic evaluation, and RAG
• Excellent written and verbal communication skills

Please refer the Job description for details