In this senior technical leadership role you will define our model evaluation strategy and determine and develop appropriate methodologies to improve model accuracy. Our goal is to deliver offline evaluation insights that drive model development and user experience improvements while upholding the privacy and quality standards that Apple is known for. With the advent of Apple Intelligence you will face novel challenges in developing representative datasets building realistic simulation environments and developing scalable end-to-end evaluation pipelines that can evolve rapidly with changes in system architecture. As Siri becomes more personal you will develop innovative evaluation solutions that are grounded in representative personal contexts. As a senior engineering leader you will cultivate relationships with stakeholders across Siri and Apple to adopt state-of-the-art approaches to model evaluation and to adapt them to the unique needs of specific Siri models and systems. Your leadership will be instrumental in fostering a culture of continuous improvement and data-driven decision-making across Siri teams.
10 years of technical leadership experience
Experience with Machine Learning model development and evaluation
Excellent understanding of an ML-based product lifecycle
Demonstrated leadership in developing AI-powered products and technologies
Proven and consistent track record of forming senior partnerships to solve complex technical problems at scale ideally for products with a global customer base
Proven record of gaining technical expertise on the job; demonstrated ability to learn new technologies independently
Experience providing technical guidance to an engineering organization
Ability to develop a long-term vision and execute strategies at scale while maintaining a grasp of technical details for better decision-making
MS/PhD in Machine Learning Computer Science or equivalent experience in a related field
Expertise in generative AI technology large language models (LLMs)
Depth of knowledge and application of statistics-based evaluation methodologies or user success metrics
Experience with full-stack development of ML-based software on consumer devices
Experience with systems engineering; in-depth understanding of interdependencies of ML and SW components
Experience in establishing and maintaining technical cross-functional partnerships across multiple organizations
Experience with technical presentations for multiple audiences including executives
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