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 |
|---|---|---|
| AIML - Sr Machine Learning Engineer, Data and ML Innovation Senior Machine Learning Engineer at Apple focused on innovating and applying state-of-the-art research in foundation models, particularly for audio data. The role involves the full ML pipeline from pre-training on large-scale unlabeled audio corpora to post-training evaluation and fine-tuning. Responsibilities include designing multi-modal data generation frameworks, building model evaluation pipelines, analyzing multi-modal data, and contributing to products with multi-modal perception data, especially audio and sensor fusion. The role also emphasizes representation learning, pre-training/fine-tuning for speech tasks, data selection techniques, and modeling data distributions. Collaboration with researchers and engineers is key, with opportunities for publishing groundbreaking research. | PretrainPost-train |
| 9 |