About this role
Be part of the next revolution in human-computer interaction. Contribute to products that are redefining mobile computing. Create groundbreaking technology for large-scale systems, speech synthesis, and generative artificial intelligence. Work with the team building the intelligent assistant that helps millions of people get things done every day.
The Siri Speech (TTS) Team is seeking a highly qualified, creative, and hands-on Early Career Research Engineer eager to advance the state-of-the-art in generative speech models and improve the user experience across Apple devices. In this role, you will collaborate with world-class product and research teams to integrate novel generative AI paradigms into on-device user experiences.
As a Research Engineer on the End-to-End Neural TTS team, your primary focus will be researching and developing techniques to enhance the steerability, expressiveness, and fine-grained control of Large Language Models and Generative Speech Decoders on Apple devices.
Minimum Qualifications
Deep Learning Foundations: Strong theoretical background in modern deep learning architectures, sequence modeling, and generative AI frameworks (e.g., Transformers, Flow Matching, Diffusion, VAEs).
Mathematical Rigor: Solid foundation in optimization, probability theory, linear algebra, and statistical machine learning.
Engineering Excellence: Excellent programming skills in Python and PyTorch, with experience writing clean, scalable code for model training and inference.
On-Device & System Awareness: Exposure to or strong interest in efficient model execution, quantization, and deployment constraints for edge/on-device computing.
Preferred Qualifications
Research Mindset: Strong problem-solving abilities, high curiosity, and a passion for exploring novel frontiers in speech and language modeling.
Degree: Master’s degree (MSc) or equivalent in Computer Science, Mathematics, Data Science, Physics, or a related quantitative field.
Experience: Early career: post-degree experience or practical industry/academic research experience demonstrated through publication, open-source codebases, or high-impact projects