Sr. Machine Learning Engineer, Siri Speech

We are seeking a candidate with a strong background in applied ML research and development, particularly in multimodal LLM, natural language processing/generation, speech generation/understanding, to join our cross-functional team focused on advancing capabilities in systems like Siri. We are looking for applied ML researchers who can develop end-to-end solutions from data scaling to necessary model implementation and training while collaborating with other engineering teams to bring research to production. You will develop and deploy novel deep learning technologies that make Siri more intelligent, natural, and useful. To succeed in this role, you should be a strong researcher and engineer, an excellent programmer, and a creative problem solver who enjoys learning new techniques, improving systems, and taking ownership of complex problems. You should also thrive as a team player in a fast-paced environment. Develop and build multilingual, multimodal LLMs to improve speech and conversational experiences for Apple customers worldwide. Advance the state of the art in natural language processing, speech and audio modeling. Design and build robust speech-centric LLM systems that enable next-generation conversational assistant features across Apple platforms. Collaborate closely with cross-functional partners in research, engineering, design, and production to deliver scalable, high-quality ML products. Stay current with emerging research and industry trends, and help define the future direction of AI-powered Siri experiences M.S. or PhD in Electrical Engineering, Computer Science or related fields 5-7+ years experience in Machine Learning Experience in developing, training/tuning large generative models or LLMs Experience with machine learning frameworks such as JAX and/or PyTorch Proficient programming skills in Python Experience in reinforcement learning Experience with Speech LLMs or other Multimodal LLMs Experience with building & deploying AI agents and LLMs Experience with large scale machine learning training/evaluation Data-centric vision and hands-on experience in developing and scaling foundation models

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