Human Factors Research Engineer, AIML Data Operations

Apple is where individual imaginations gather together, committing to the values that lead to great work. It's the diversity of our people and their thinking that inspires the innovation that runs through everything we do. Here, you'll do more than join something — you'll add something. Imagine what human factors research can do at Apple. At Apple, great ideas quickly become extraordinary user experiences. Are you excited to take on big challenges that create a positive impact for billions of people? Are you passionate about engaging directly with people to research and design elegant Apple experiences? We are looking for a talented, creative and technical user experience researcher to optimize the design of annotation workflows and analyst tools that contribute to Apple's most strategic AI initiatives. Our organization is responsible for delivering high-quality, human-annotated, machine learning data at scale for product teams across Apple. We are both an operations team and a trusted partner in the development of Apple’s consumer software and hardware experiences. This global data annotation and collection initiative enables Apple to create amazing new experiences and advance flagship experiences like Apple Intelligence and Vision Pro. Minimum Qualifications: MS or PhD in Computer Science, Human-Computer Interaction or equivalent degree 3 years of experience in mixed-methods human factors research, especially in evaluating user interface designs Expert in qualitative and quantitative experimental design, statistical data analysis, and reporting Excellent listening, communication and interview skills in both individual and focus group settings Experience with human-in-the-loop evaluation, data annotation workflows, or AI/LLM model development Preferred Qualifications: Proactive, curious, data-driven, and collaborative Proficient in data exploration and data analysis languages or tools such as SQL, Python, or R Studio Industry experience working with UX designers and software engineers to develop highly-usable and performant interfaces Published research in human-in-the-loop evaluation or data annotation workflows