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 |
| Machine Learning Engineer, Proactive Machine Learning Engineer focused on developing, fine-tuning, and evaluating Large Language Models for various NLP tasks like summarization, question answering, and search relevance, with a strong emphasis on transferring cutting-edge generative AI research into production-ready technologies for Apple's AI-powered products. |
| Post-trainAgent |
| 9 |
| Machine Learning Engineer, Apple Services Engineering Machine Learning Engineer at Apple Services GenAI & ML Frameworks team, focusing on bridging foundation model capabilities with real-world production systems. The role involves LLM continual pretraining, posttraining, agentic reinforcement learning, and agentic system optimization to improve LLM domain knowledge, tool use, reasoning, and system integration for user-facing features at scale. | Post-trainAgent | 9 |
| Machine Learning Architect - Conversational Speech Machine Learning Architect for Conversational Speech at Apple, responsible for defining modeling strategy and technical direction for speech recognition, synthesis, dialog systems, multimodal foundation models, and speech-to-speech technologies. The role involves hands-on technical leadership, translating research into production-quality systems at scale, and ensuring architectural decisions align with on-device constraints, latency, and scalability. | Post-trainAgent | 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 |
| Senior Computer Vision and Machine Learning Engineer, Creator Studio Senior Engineer to work on Generative AI for creative editing tools, focusing on computational photography and multi-modal image editing. Responsibilities include incubating ML algorithms, owning the model lifecycle (training to inference), designing data pipelines, and communicating research. Requires MS/PhD with 5+ years experience, deep ML knowledge (multimodal LLMs, MoE, PEFT, RLHF), and experience delivering customer-facing CV/GenAI products. | Post-trainServe | 9 |
| Senior Computer Vision and Machine Learning Engineer, Creativity Apps Senior Computer Vision and Machine Learning Engineer at Apple focused on Generative AI for creative editing tools. The role involves incubating, training, evaluating, and deploying ML models, particularly diffusion models, transformers, and GANs, with a focus on computational photography and multi-modal image editing. The engineer will also work on efficient inference and collaborate with cross-functional teams to bring these technologies to Apple products. | Post-trainServe | 9 |
| Sr. ML Production Model Automation Engineer, Siri Speech This role focuses on automating the production model lifecycle for Siri's speech and audio features, which are powered by multimodal, on-device AI. The engineer will build and operate agent-based automation pipelines for ML model training, iteration, staging, rollout, and deprecation, including SFT, LoRA, and RL phases. The work involves developing multi-agent workflows for evaluation, triage, and root cause analysis, and owning the launch tooling for training jobs. | Post-trainServe | 8 |
| Machine Learning Engineer — Generative Models, Productivity Apps Machine Learning Engineer focused on generative models for productivity apps, involving design, training, evaluation, and end-to-end feature delivery from research prototype to production. Requires experience with generative models like diffusion and transformers, and programming in PyTorch or JAX. | Post-trainServe | 8 |
| Video Codec Machine Learning Engineer, Audio & Media Technologies The role focuses on developing next-generation video codecs by applying machine learning, deep learning, neural video compression, generative AI, and computer vision. It involves contributing algorithms to industry standards and collaborating with software and hardware teams. The role emphasizes ML-driven approaches to video coding and processing. | Post-train | 8 |
| Sr. Machine Learning Research Engineer, Siri Speech Machine Learning Research Engineer for Siri Speech team, focusing on creating and productizing ML algorithms for Speech Recognition, Speech Synthesis, and Conversational AI on Apple devices and cloud infrastructure. Requires expertise in compiled languages, ML frameworks, and applied ML, with the ability to translate research into user-facing products. | Post-trainServe | 8 |
| Machine Learning Engineer, Siri Speech Machine Learning Engineer on the Siri Speech team at Apple, focusing on designing, developing, and implementing ML models for speech, NLP, and multimodal applications. This role involves fine-tuning deep learning systems for speaker recognition and multimodal understanding, integrating ML solutions into production at scale, and working with large datasets to build production-quality models. The position requires strong Python skills, experience with ML algorithms and deep learning frameworks like TensorFlow/PyTorch, and knowledge of speech/audio processing. | Post-trainServe | 8 |
| Machine Learning Engineer, Siri Attention & Invocation Machine Learning Engineer for Siri's Attention & Invocation team, focusing on on-device voice invocation experiences. Responsibilities include building and deploying models, end-to-end training and evaluation pipelines, and optimizing speech/audio processing for Apple devices. Requires strong ML/DL background, speech recognition experience, and software engineering skills. | Post-trainServe | 8 |
| Video Machine Learning Engineer, Audio & Media Technologies Video Machine Learning Engineer at Apple, focusing on designing, developing, and deploying ML solutions for video processing, understanding, and enhancement. The role involves researching, prototyping, training, evaluating, and optimizing models for on-device deployment across various Apple products. It bridges deep learning research with real-world product impact. | Post-trainServe | 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 |
| Senior Machine Learning Engineer - Ads Relevance & Quality Senior Machine Learning Engineer at Apple Ads focusing on Ads Relevance & Quality. The role involves designing and implementing ML models, particularly NLP and multi-modal models, to evaluate ad relevance, detect inappropriate content, and optimize user satisfaction. It requires experience with Transformer-based architectures, fine-tuning LLMs, and large-scale ML deployment in domains like ad tech or content moderation. | Post-train | 7 |
| Internationalization Engineering Manager - Intelligence Features Engineering Manager for an International Intelligence Features team at Apple, focusing on leading ML engineers to build and evaluate international features, processes, and tooling using LLMs for localization and internationalization tasks. The role involves hands-on technical leadership, strategic roadmap definition, and managing a team to deliver end-user intelligence features across multiple platforms. | Post-trainServe | 7 |
| Engineering Project Manager - AI Features Internationalization, L&RE Engineering Project Manager responsible for the internationalization and global launch of AI-driven products, including Apple Intelligence. This role involves leading technical integration of generative AI and ML features across multiple languages and countries, managing international data generation, and driving model evaluation strategy for audio, vision, and language models. | Post-trainData | 7 |
| Senior Apps Applied Scientist This role focuses on designing, developing, and implementing sophisticated machine learning and AI models for creative applications, particularly image editing apps. It involves building end-to-end ML pipelines, prototyping AI features, developing AI tools, and deploying models into production. The role requires strong Python programming, experience with large-scale data, causal inference, LLM fine-tuning, RAG, and AI framework development. | Post-trainServe | 7 |
| Video Machine Learning Engineer, Audio & Media Technologies Video Machine Learning Engineer at Apple, focusing on developing and integrating ML/DL solutions for video coding and processing in Apple products. Responsibilities include designing, training, optimizing, and integrating neural networks, as well as enhancing user experiences in video communication. Requires a Master's degree or equivalent experience in EE/CS, digital video processing, modern video codecs, and neural network design/optimization. | Post-train | 7 |
| Software Engineer, Siri Attention And Invocation Software Engineer for Apple's Siri team, focusing on improving speech recognition, natural language understanding, and dialogue management through advanced statistical techniques and core machine learning algorithms. The role involves end-to-end feature development, collaboration with cross-functional teams, and shipping user-facing features to millions of users. | Post-trainAgent | 7 |