AIML – Senior Machine Learning Engineer, Siri and Information Intelligence Full-time

Summary
As part of Apple Intelligence, Siri team is at the forefront of the next revolution in machine learning. Our innovative product redefines computing by using powerful technologies in speech and audio domain. We are dedicated to creating groundbreaking conversational assistant technologies for both large-scale systems and new client devices, building upon our legacy of intelligent assistant solutions that already assist millions of users worldwide. We are seeking a highly skilled Senior Machine Learning Engineer specializing in speech and audio technologies to join our dynamic team. The ideal candidate will play a pivotal role in the evaluation and enhancement of our product’s speech and audio capabilities. They will collaborate closely with multi-functional teams to implement and optimize speech and audio, ensuring the highest level of quality, performance, accuracy, and scalability. This role offers a great opportunity to contribute to the advancement of AI systems and shape the future of computing. As a Machine Learning Engineer within the Siri team, you will define new approaches for evaluating speech and audio to enhance product capabilities. You will collaborate with various language engineers, modeling teams, and product managers to conduct thorough evaluation and analysis of speech and audio. Your expertise in defining and measuring the quality and intelligibility of a speech signal will help communicate, evaluate, and iterate on innovative deployed models and predictors.

Description
This Siri team leads the development of advanced evaluation methodologies for ML based systems, model interpretability, and experimentation, to ensure that every release delivers an improved Siri user experience. The team is searching for versatile ML Engineers to work with a passionate, product-focused team to define innovative approaches for evaluating ML based systems, and model interpretability. You will run experiments, statistically interpret data with a mind on causation, data visualization, plus designing, building, and evaluating models.