The Intelligence Platform team empowers clients across Apple’s operating systems with high quality user-centric knowledge and inferences that enable next generation user experiences. We’re an applied Machine Learning team that leverages state of the art technologies like generative AI, graph machine learning, and private learning to deliver high quality inferences. We are in search of an accomplished and driven Machine Learning Engineer who has a robust understanding of Large Language Models and Generative AI. Your primary role will involve decoding and applying groundbreaking research and setting the course for the team in this thriving field. By contributing to our team, you’ll play an integral part in developing Siri, Photos, Music, and various other services, leaving a significant footprint on the evolution of our AI platforms! Join us in our exciting journey as we push the frontiers of Machine Learning and Generative AI. Your expertise and contributions will be invaluable in shaping the future of our Intelligence Platform!

As a Machine Learning Engineer on the Knowledge Platform team, you’ll join a phenomenal team of talented engineers and researchers and will be entrusted with a range of responsibilities. Your tasks will include:

– Leading the exploration and application of Large Language Models and Generative AI, venturing into areas within these fields such as Question-Answering over structured databases.

– Translating the latest research into high-performing systems and models that can be practically applied to enhance user experiences.

– Setting the team’s strategic direction, cultivating an environment that encourages innovation and professional growth.

– Actively engaging in all aspects of model development, from ideation and experimentation to deployment.

– Collaborating with various teams to develop and implement Machine Learning solutions, ensuring performance optimization and alignment with broader business goals.

An advanced degree (Master’s or Ph.D.) in Computer Science, Artificial Intelligence, Machine Learning, or a related field is preferredHands-on experience with LLM fine-tuning approaches and training models.