Sr. Machine Learning Engineer, Siri Speech

Join the team redefining what a deeply personal and integrated assistant can be.

As part of the Siri organization, you will help shape one of the world's most widely used AI assistants, powered by our next-generation of Apple Intelligence, with capabilities like personal context understanding and on-screen awareness, built with privacy from the ground up. Your work will have direct, meaningful impact for users across iOS, iPadOS, macOS, watchOS, and visionOS.

This is a rare opportunity to build at the intersection of cutting-edge AI and human-centered design, shipping technology that is centered around users and their needs.

We are seeking a candidate with a strong background in applied ML research and development, particularly in multimodal LLM, natural language processing/generation, speech generation/understanding, to join our cross-functional team focused on advancing capabilities in systems like Siri. We are looking for applied ML researchers who can develop end-to-end solutions from data scaling to necessary model implementation and training while collaborating with other engineering teams to bring research to production. You will develop and deploy novel deep learning technologies that make Siri more intelligent, natural, and useful. To succeed in this role, you should be a strong researcher and engineer, an excellent programmer, and a creative problem solver who enjoys learning new techniques, improving systems, and taking ownership of complex problems. You should also thrive as a team player in a fast-paced environment.

Minimum Qualifications

• M.S. or PhD in Electrical Engineering, Computer Science or related fields
• 5-7+ years experience in Machine Learning
• Experience in developing, training/tuning large generative models or LLMs
• Experience with machine learning frameworks such as JAX and/or PyTorch
• Proficient programming skills in Python

Preferred Qualifications

• Experience in reinforcement learning
• Experience with Speech LLMs or other Multimodal LLMs
• Experience with building & deploying AI agents and LLMs
• Experience with large scale machine learning training/evaluation
• Data-centric vision and hands-on experience in developing and scaling foundation models