Audio Machine Learning Engineer

Summary
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.

Responsibilities

• Work with unique, proprietary datasets - developing algorithms to process them and devising metrics to evaluate and improve quality
• Design and implement machine learning models focused on the audio domain, for low-latency feedback and high-fidelity streaming
• Drive data quality insights and influence the design of our end-to-end system
• Conduct both cutting-edge research and product-oriented development
• Collaborate closely with researchers, engineers, and product teams to bring new capabilities to life
  
  

• 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
  
  

• 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