Machine Learning Engineer - Speech

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

Preferred Qualifications
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

Minimum Qualifications
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