Audio Machine Learning Engineer

Apple's Hardware group is home to some of the world's most advanced technologies - powering the experiences that define Apple products. We are looking for a talented and curious Audio Machine Learning Engineer to join our growing Machine Learning team in Herzliya. In this role, you will help create the full data lifecycle that underpins our models: from designing what data we collect, through curation and quality monitoring, to running rigorous experiments that drive model improvements. You will work closely with other ML and Data Engineering teams to ensure our models are trained on the best possible data, reaching the best accuracy, and that we deeply understand when and why they don't perform as expected.

Description

Redefine the future of human-computer interaction and the way people communicate. Contribute to products that shape mobile computing and create breakthrough technologies in the audio domain.
In this role, you will push the boundaries of audio solutions across the full stack - from data pipelines and model training to optimization for Apple silicon. You'll collaborate with world-class researchers and engineers to ship technology that reaches hundreds of millions of users, while upholding Apple's unwavering commitment to privacy.

Minimum Qualifications

BS or MS in CS, EE, or related degree
3+ years of industry experience in deep learning through applied research roles
Deep understanding of Machine Learning fundamentals
Proficiency in Python and at least one deep learning framework (PyTorch, TensorFlow, or JAX)
Collaborative skills for dependable and consistent steering of novel research alongside fellow teams

Preferred Qualifications

Ph.D. in CS, EE or a related field
Advanced background and hands-on experience in speech ML technology (e.g., multi-modals, speaker embeddings, voice isolation, ASR, multichannel sensor fusion, generative speech)
Background in digital signal processing (DSP) for audio signals
Experience training large models using both supervised and self-supervised methods
Track record of shipping ML features in a production environment