Machine Learning Architect - Conversational Speech

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401(k) match

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About this role

Machine Learning Architect - Conversational Speech Cupertino, United States of America The Speech organization within Siri drives major speech recognition, synthesis, and speech-to-speech model advances for features deeply embedded throughout Apple's ecosystem. Our mission is to build cutting-edge infrastructure, datasets, and models that empower Siri conversational AI, dictation, and speech-enabled Apple Intelligence features across natural language understanding, dialog generation, speech recognition, and multimodal interaction. We apply these technologies to create engaging, intelligent, and personalized conversational experiences for millions of Apple users. We are seeking a Machine Learning Architect to serve as a senior technical leader spanning the full Speech organization. You will set the future modeling direction for all of conversational speech-charting the architectural and algorithmic course for how Apple's speech technologies evolve. You will operate as a hands-on expert who not only defines strategy but also digs into the hardest technical problems, working shoulder-to-shoulder with teams to overcome critical obstacles. Reporting directly to the Speech organization leadership, you will have broad visibility and influence across speech recognition, synthesis, dialog, multimodal foundation models, and speech-to-speech systems, ensuring coherent technical vision and cross-team alignment. As the Machine Learning Architect for Conversational Speech, you will define modeling strategy and technical direction across the Speech organization, establishing a unified architectural vision for speech recognition, speech synthesis, dialog systems, multimodal foundation models, and speech-to-speech technologies. You will serve as the organization's foremost modeling expert, providing deep technical guidance to multiple teams working on interconnected speech capabilities. You will evaluate emerging research and industry trends-including

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