Machine Learning Engineer - Speech

Summary
Play a part in shaping the future of human-computer interaction. Contribute to products that are redefining mobile computing and creating breakthrough technologies in conversational AI, speech recognition, and natural language understanding and generation.

Description
We are seeking a passionate and experienced Machine Learning Engineer to join our team. In this role, you will push the boundaries of on-device speech recognition and NLP, working 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 spanning speech recognition and NLP domains
• 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
  
  

• M.Sc. in Computer Science or a related field, or equivalent practical experience
• Deep understanding of Machine Learning fundamentals
• Proficiency in Python and at least one deep learning framework (PyTorch, TensorFlow, or JAX)
• 3+ years of industry experience in deep learning through applied research roles
• Hands-on experience with the full deep learning lifecycle at scale, including dataset curation, architecture design, distributed training, error analysis, and production deployment.
• M.Sc. in Computer Science or a related field, or equivalent practical experience
  
  

• Ph.D. in Computer Science or a related field
• Advanced background and hands-on experience in speech technology (e.g., ASR, TTS, speaker recognition)
• Background in NLP, including language modeling, text processing, and linguistic understanding
• Experience training large models using both supervised and self-supervised methods
• Track record of shipping ML features in a production environment