AIML - Applied ML Research Scientist - Accessibility, Human Centered Machine Intelligence

We are seeking an Applied Machine Learning Research Scientist to join our Accessibility efforts on the Human-Centered Machine Intelligence (HCMI) team within AIML at Apple. Research Scientists in HCMI tackle significant technical challenges, collaborate with partner teams to ship products, and shape the field of human-centered AIML via external publication. Join us to advance ML research and intelligent interactive technologies that enable people of all abilities to use Apple technology. The Human-Centered Machine Intelligence (HCMI) group advances ML modeling and interactive technologies grounded in human-centered research. We are a close-knit team of research scientists and engineers working closely with product and platform teams across Apple. The Accessibility team within HCMI specifically focuses on core ML technologies that address grand challenges in accessibility, including equitable speech access for people with communication disabilities, visual understanding for people who are blind or low vision, sign language technology, and more. We are especially interested in researchers who want to bring expertise in one or more of the following areas to these accessibility challenges: multimodal reasoning, agentic systems and multi-turn interaction, contextual understanding, privacy-preserving ML, and synthetic data and evaluation methodologies. Minimum Qualifications: PhD in Machine Learning, Computer Science, Human-Computer Interaction or related field. Experienced researcher, with research depth in one or more of: multimodal modeling, agentic systems and tool use, computer vision, speech, or foundation model post-training and evaluation. Publication and research record commensurate with seniority. Proven history of turning research contributions into tools, datasets or products, or strong potential to do so. Preferred Qualifications: Experience conducting research in accessibility or demonstrated commitment to accessibility. Experience doing data collection or data curation (e.g., developing data or annotation protocols). Experience with human-centered evaluation methods (e.g., developing user study protocols or running studies). Experience developing and shipping products that use machine learning. Strong writing and presentation skills.