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, SIML Machine learning research engineer with experience building modern generative models based on diffusion and auto-regressive technologies. Focus on training and adapting large scale image/video/audio/multimodal foundation models, staying at the forefront of AI research, and pioneering proprietary ideas for Apple's ecosystem. Experience with training and fine-tuning modern image/video generation models is required. | Post-trainPretrain | 9 |
| Sr. Machine Learning Research Engineer, Siri Speech This role focuses on advancing Siri's conversational AI capabilities by developing and deploying novel deep learning technologies for efficient speech and multi-modal modeling. The primary goal is to improve Siri's intelligence, naturalness, and usefulness, with a strong emphasis on efficient model deployment on servers and devices, minimizing latency, and preserving privacy. The role involves research and development with a track record of publications or product application in efficient deep learning. |
| Post-trainServe |
| 9 |
| AIML - Machine Learning Engineer in Foundation Models, Responsible AI and Safety The role focuses on applied research in responsible AI and safety for foundation models, including training, evaluation, alignment, and mitigations for deployment in Apple products. It involves collaboration with researchers and engineers to develop and deliver AI technologies that uphold Apple's values and privacy standards. | Post-trainAgent | 9 |
| Machine Learning Engineer — Camera & Photos, Creative Foundations Machine Learning Engineer and Researcher to join the Creative Foundations team within Camera & Photos. This role involves inventing novel ML models at the intersection of research and product features, focusing on image understanding for consumer-facing applications. Responsibilities include designing architectures, training strategies, and intelligent systems, translating research into shippable features, and leveraging interpretability techniques. Requires MS/PhD, experience in ML/computer vision, proficiency in ML frameworks, and understanding of modern ML architectures. Preferred qualifications include a track record of creative problem-solving, published research, and specific computer vision experience. | Post-trainServe | 9 |
| Camera Software- Sr. Machine Learning Research Engineer Research Engineer role focused on developing and fine-tuning generative models for Apple's camera software, specifically for computational photography applications on iPhone and iPad. The role involves designing model architectures, training strategies, building data pipelines, and developing evaluation frameworks, with a strong emphasis on vision and image generation. | Post-trainData | 9 |
| Senior Applied ML Scientist – Generative Video This role focuses on researching, designing, and training state-of-the-art generative video models, primarily diffusion-based, with applications for creative users. It involves exploring novel architectures, spatiotemporal modeling, and multi-modal conditioning, aiming for real-world product impact. | Post-train | 9 |
| AIML - Senior ML Researcher in Foundation Models, Responsible AI Senior ML Researcher in Foundation Models, Responsible AI. Focus on research and application of ML methods for breakthrough user experiences while upholding Apple's values, privacy, and quality standards. Will define and deliver responsible ML technologies, develop methods to train and evaluate foundation models with responsibility and safety in mind, research safety alignment and model robustness, and develop mitigations for safe LLM deployment. | Post-trainAgent | 9 |
| AIML - ML Researcher, Responsible AI Research role focused on responsible AI, fairness, and safety of Generative AI, including red teaming, developing mitigations, and evaluation frameworks for LLMs and foundation models within consumer products. | Post-trainAgent | 8 |
| AIML - Research Scientist, AI Interpretability & Visualization Research Scientist focused on AI interpretability and visualization, developing tools and strategies to make AI systems more understandable, transparent, and safe. The role involves defining research directions, implementing new tools, investigating AI algorithms, and contributing to projects that ship on Apple devices. | Post-trainEval Gate | 8 |
| 3D Computer Vision Research Engineer, Apple Maps Research Engineer for Apple Maps focusing on 3D computer vision and ML to extract map features from imagery and sensor data. Develops models for visual understanding, scene understanding, and physical-world reasoning, combining ML, 3D geometry, and multimodal data for large-scale mapping problems. | Post-trainAgent | 7 |