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 Systems Engineer, Siri Agent Modeling Machine Learning Systems Engineer for Siri, focusing on optimizing model training and inference for generative AI technologies on Apple Silicon. This role involves working across the ML stack, from training to deployment, to deliver production-level code for models impacting millions of users. | ServePost-train | 9 |
| Senior Machine Learning Engineering Manager – Ads Predictions This role is for a Senior Machine Learning Engineering Manager at Apple, focusing on Ads Predictions. The manager will lead a team responsible for building and scaling complex ML models for response prediction (CTR, conversion rate) under latency constraints. Key responsibilities include driving the development and deployment of state-of-the-art models, owning the full ML lifecycle from data to production serving, and championing privacy-preserving ML approaches. The role requires strong leadership, cross-functional collaboration, and hands-on experience with large-scale ML systems, including neural networks and LLM-based systems. |
| ServePost-train |
| 8 |
| Senior Machine Learning Engineer, Analytics & Data Engineering Senior Machine Learning Engineer focused on building and innovating AI/ML foundations for Apple's services, ensuring security, privacy, and scalability. The role involves researching and developing state-of-the-art AI/ML solutions, designing secure and performant systems, and collaborating with broader teams. Requires strong engineering foundations, experience with distributed systems, and modern AI/ML frameworks. | Serve | 7 |
| Software Development Engineer, Intelligence Platform - Proactive Software Development Engineer role focused on building the on-device intelligence platform for Apple products, powering Generative AI experiences like Apple Intelligence. Responsibilities include developing knowledge serving sub-systems, APIs, and on-device data processing runtime and storage, shipping code that runs on millions of devices. | Serve | 7 |
| Machine Learning Engineer - iCloud Anti-Abuse Machine Learning Engineer for iCloud Anti-Abuse team at Apple, focusing on building and deploying ML models for abuse detection (spam, phishing) at massive scale. The role involves the full ML lifecycle from data pipelines and feature engineering to model training, low-latency inference, and monitoring within distributed systems. | ServeData | 7 |
| Sr./Staff ML Infrastructure Engineer, Compute (TPU Scheduling) - Foundation Model This role focuses on designing and developing scheduling and orchestration systems for large-scale TPU workloads in multi-region clusters, supporting foundation model training and inference. It involves distributed systems, cluster management, and performance optimization. | Serve | 7 |
| Software Engineering Manager, Wallet Identity Engineering Manager for Wallet Identity Services at Apple, focusing on leading a team that builds and operates ML-powered services for liveness detection, face matching, and image quality. The role involves full lifecycle ownership of these services, from architecture and development to deployment and monitoring, with a strong emphasis on MLOps and scaling ML models in production. | ServePost-train | 7 |
| Sr Engineering Program Manager, AI/ML Technical Program Manager to help shape the future of scalable machine learning infrastructure. Lead planning, execution, and cross-functional coordination across engineering teams to deliver robust, high-performance ML systems that operate across Apple’s cloud and distributed environments. Define and run processes that ensure the timely delivery of GPU, compute, and data infrastructure components that support large-scale ML workloads. | ServeData | 7 |
| Location Estimation Scientist, Sensing & Connectivity This role focuses on building and maintaining production-grade software systems and ML models for location intelligence using sensor data. It involves the full ML lifecycle from problem formulation to production deployment, with a strong emphasis on signal processing, data infrastructure, and scaling ML to hundreds of millions of devices. | Serve | 7 |
| Machine Learning (MLOps) Engineer MLOps Engineer role focused on building and optimizing ML infrastructure, ensuring reliability, scalability, and continuous improvement of AI/ML systems in production. Responsibilities include end-to-end quality initiatives, automated pipelines for training, evaluation, deployment, and championing model observability and governance. | ServeEval Gate | 7 |
| On-Device ML Infrastructure Engineer (CoreML Runtime), Graphics, Games & ML This role focuses on building and maintaining the Core ML Runtime for on-device execution of ML models on Apple products. The engineer will work on the ML graph compiler, runtime, and kernels, optimizing model execution for performance, energy efficiency, and thermal management. The role involves developing production-critical system software for implementing ML models on Apple Silicon, with a focus on common compiler optimizations and runtime systems. | Serve | 7 |
| On-Device ML Infrastructure Engineer, ML User Experience, APIs & Integration, Graphics, Games & ML This role focuses on building the ML infrastructure and developer experience for running ML models on Apple devices. It involves developing APIs for ML model conversion and authoring, optimizing models for efficiency and performance, and integrating ML tools into repositories. The goal is to enable efficient ingestion and implementation of models within Apple's ML stack, impacting various core experiences like Camera, Siri, and Health. | Serve | 7 |
| On-Device ML Compiler Engineer, Model Compilation, Graphics, Games & ML This role focuses on building and optimizing ML compilers and runtimes for on-device execution across Apple's diverse hardware (Neural Engine, GPU, CPU). It involves working with MLIR-based compiler stacks to improve runtime performance and enable efficient execution of ML models on Apple devices, impacting core experiences like Camera, Siri, and Health. | Serve | 7 |
| On-device ML Infrastructure Engineer, Compiler & Runtime, Graphics, Games & ML Seeking an experienced ML Infrastructure Engineer to build and optimize the execution engine and compilation toolchain for on-device ML models on Apple products. This role focuses on creating efficient, portable, and extensible runtimes and compilers, connecting compiler technology, runtime components, kernel libraries, and hardware compilers to enable ML execution across various devices. | Serve | 7 |
| On-device ML Integration Engineer, Graphics, Games & ML This role focuses on integrating ML models into Apple's on-device inference stack, optimizing performance, and ensuring functionality across various Apple devices. It involves working with ML frameworks, compilers, and hardware targets to enable efficient and private AI experiences. | Serve | 7 |
| Senior ML Software Engineer, Watch Software Senior ML Software Engineer for Apple Watch, focusing on developing and deploying ML models on-device using multimodal sensor data. The role involves the full ML lifecycle from research to productization, with an emphasis on power-efficient, on-device inference for consumer features. | ServePost-train | 7 |
| Staff Machine Learning Engineer : Platform Intelligence - Apple Maps Staff Machine Learning Engineer for Apple Maps focused on designing, developing, and deploying on-device ML models. This role requires optimizing for performance on Apple platforms, collaborating cross-functionally, and mentoring junior engineers. Experience with ML frameworks, systems programming, and shipping production ML models on mobile/embedded devices is critical. | ServePost-train | 7 |
| Machine Learning Systems Engineer, Siri Runtime Systems and Interaction Machine Learning Systems Engineer for Siri at Apple, focusing on integrating, optimizing, and deploying ML models into production software pipelines for resource-constrained platforms. The role involves building infrastructure for ML evaluation and analysis, collaborating with ML engineers, and ensuring performance and reliability of ML workloads within the Siri ecosystem. | ServeEval Gate | 7 |
| Senior Machine Learning Platform Engineer - AI, Search & Knowledge Senior Machine Learning Platform Engineer at Apple, focused on building and scaling the AI, Search & Knowledge platform. This role involves creating seamless integrations between ML frameworks and the platform, designing Python SDKs and APIs, and building backend services to support model management and serving infrastructure. The goal is to enable ML practitioners to focus on innovation by abstracting infrastructure complexity. | Serve | 7 |