Q - Machine Learning Engineer - Speech
Software Engineering, Data Science
Posted on Oct 30, 2026
Our team is new, growing, and evolving quickly, and we are hiring across multiple disciplines. We work at the intersection of machine learning, hardware, and software, creating technologies that reshape how people communicate and experience the world around them in everyday life.
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.
- 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