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 |
|---|---|---|
| Machine Learning Engineer, Apple Intelligence ML Engineer focused on building and optimizing generative models for international languages, covering the full pipeline from data preprocessing to deployment. | Post-trainServe | 9 |
| AI/ML Software Engineer - SES Gen AI Solutions, IS&T AI/ML Engineer to design, develop, and deploy intelligent solutions for modern contact center platforms, focusing on scalable AI systems for chatbots, voice assistants, speech analytics, and automated customer support workflows. The role involves building end-to-end AI pipelines, including model development, deployment, and optimization, with deep expertise in LLMs, conversational AI, and real-time inference systems. | Agent |
| 8 |
| AI/Machine Learning Engineer AI/ML Engineer to build intelligent systems and deploy state-of-the-art AI models and systems across Apple's business groups. Responsibilities include implementing ML infrastructure, developing feature engineering and fine-tuning frameworks, designing ML pipelines, and optimizing models. The role requires designing systems from raw data to autonomous action, implementing RAG pipelines, working with embeddings and vector databases, building AI agents with tool use, and fine-tuning transformer models. Experience with large-scale data and ML frameworks is essential. | AgentData | 8 |
| Senior Machine Learning Engineer, Search & AI Senior Machine Learning Engineer focused on developing next-generation Search and Question Answering systems using cutting-edge search technologies and large language models. The role involves improving Query Understanding, Retrieval, and Ranking, leveraging fine-tuning, reinforcement learning, embeddings, deep learning, and online learning. Experience with RAG, retrieval, and generative LLMs is crucial. | AgentServe | 8 |
| Senior Engineering Manager - Applied Machine Learning - AiDP Engineering Manager to lead and scale the Search / Information Retrieval team, focusing on building and scaling enterprise platforms that leverage Search & GenAI technologies. The role involves bridging legacy IR with modern GenAI for intelligent information ecosystems, managing inference systems, and ensuring high performance and low latency. | AgentServe | 7 |
| Reliability Engineer Software Engineer on the Applied Machine Learning Team to architect and orchestrate high-performance, scalable enterprise platforms for Data, ML, and inferencing. Focus on ensuring availability, performance, and low latency for high-throughput applications. Manage diverse workloads across ML/Data/Inference platforms and evaluate new technologies. | ServeData | 7 |
| Data Scientist This role focuses on building and scaling automated insight pipelines for the sales organization, developing ML models for opportunity detection and performance diagnosis, and embedding these insights into AI agents, dashboards, and GenAI tools. The role involves end-to-end insight development, including data preparation, statistical analysis, LLM prompt engineering, and deploying ML models for forecasting, anomaly detection, attribution, and causal inference. It also includes building RCA and recommendation engines, analyzing agent interactions, implementing LLM evaluation pipelines, and supporting experimentation. The role requires partnering with AI engineers and PMs, acting as a data translator, influencing upstream data model design, and driving KPI definitions. | AgentEval Gate | 7 |