Apple has 261 active AI-related job listings. The majority of these roles are focused on agents, accounting for 24% of the total, followed by application (22%) and serving infrastructure (21%). Engineering is the primary function for these positions, with the United States being the dominant hiring country. Frequent tech tags include model serving, inference infrastructure, and LLM observability. Over the last 30 days, Apple has posted 111 new AI roles, representing a 61% increase compared to the previous 30-day period.
Currently tracking 171 active AI roles, down 37% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $120k–$487k (avg $235k).
Apple currently has 233 active AI-related roles in our index. The most common open titles are: Machine Learning Engineer (4), AIML - Sr Data Scientist, Evaluation (2), Advanced Manufacturing Engineer(iPhone) - Smart Manufacturing (2), Machine Learning Engineer, Apple Services Engineering (2), Machine Learning Software Engineer (2). Most positions are in Engineering and Research.
Apple's active AI hiring is concentrated in: agents (30%), application (21%), serving infrastructure (14%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Apple is hiring AI talent in: United States (182 roles), China (17 roles), India (10 roles), United Kingdom (7 roles).
Job postings at Apple most frequently mention: Machine Learning, Python, Data Science, Large Language Models (LLMs), Statistics.
In the past 30 days, Apple has posted 80 new AI-related roles.
| Title | Stage | AI score |
|---|---|---|
| Sr Technical Product Manager, AI & Data Platforms (AiDP) Sr. Technical Product Manager for Apple's AI & Data Platforms team, focusing on building and managing GenAI capabilities for enterprise business units. The role involves defining product strategy, roadmaps, and user experiences for GenAI inferences, knowledge discovery, conversational AI, and workflow automation, working closely with engineering, data science, and business stakeholders to drive adoption and measure success. | Ship | 7 |
