What are the responsibilities and job description for the Applied AI Scientist position at Apple, Inc.?
We're idealists. Inventors. Forever tinkering with products and processes, always on the lookout for better. Whether you work at our global offices, offsite, or even at home, a job at Apple will be demanding. But it also rewards forward-thinking, creative thinking and hard work. And none of us here would have it any other way. Does an exciting, dynamic, and fast-paced environment catch your attention? Do you like puzzles and determining solutions that are not obvious? Terrific! Consider joining our team! The Applications team is looking for an outstanding Applied AI Scientist who will strengthen our team's capabilities in statistical modeling, machine learning, and foundational AI development. This role will drive innovation in building scalable ML and AI solutions that enhance our product intelligence, improve automation, and expand our AI-driven capabilities across business domains.
The Applied AI Scientist will work on designing, developing, and implementing sophisticated machine learning and AI models to solve complex problems, particularly for creative applications like our video editing apps. The role involves building end-to-end ML pipelines, prototyping novel AI-powered features, developing AI tools, and collaborating closely with engineering, product, and marketing partners to bring intelligent solutions into production. The ideal candidate combines deep technical expertise in machine learning, statistical modeling, and AI framework development with strong problem-solving and interpersonal skills, ensuring effective collaboration and measurable impact in a fast-paced environment.
PhD in Computer Science, Statistics, Mathematics, or a related quantitative field with 3 years of relevant experience; or MS with 5 years of experience in applied AI, machine learning, or statistical modeling.\n3 years of programming proficiency with Python for data science and AI (e.g., Pandas, Scikit-learn, NumPy).\n3 years of hands-on experience applying statistical modeling and machine learning algorithms for supervised and unsupervised learning (classification, regression, clustering, etc.).\n3 years of experience working with large-scale data and distributed systems (e.g., Hadoop, Spark).\nWorking familiarity with deep learning algorithms (CNN, RNN, Transformers) and frameworks (TensorFlow, PyTorch).\nWorking familiarity with causal inference models and techniques.\nHands-on experience deploying AI/ML models into a production environment.\nExperience with rapid prototyping, reproduction, and validation of research ideas.\nExperience developing or contributing to AI frameworks, APIs, or internal tools used by other teams.\nExcellent analytical, communication, and collaboration skills across multi-functional teams.\n
Experience with LLM fine-tuning, prompt engineering, or retrieval-augmented generation (RAG).\nFamiliarity with generative AI techniques (e.g., diffusion models).\nKnowledge of computer vision (CV), multimodal models, and video generation/understanding, and their application in video or image analysis.\nExperience building scalable AI/ML systems using cloud platforms (AWS, Google Cloud Platform, Azure) and MLOps tools (e.g., SageMaker, Vertex AI, MLflow).\nExperience applying statistical and ML techniques to analyze user behavior, content usage, conduct feature analytics, and derive product insights.\nHands-on experience with production model monitoring, including tracking data/model drift and running diagnostics.\nProven ability to translate complex research ideas into scalable, production-level AI solutions.\nStrong software engineering practices, including version control, testing, and code review.\nFamiliarity with cross-domain applications of AI/ML (e.g., marketing analytics, personalization, recommendation systems).
The Applied AI Scientist will work on designing, developing, and implementing sophisticated machine learning and AI models to solve complex problems, particularly for creative applications like our video editing apps. The role involves building end-to-end ML pipelines, prototyping novel AI-powered features, developing AI tools, and collaborating closely with engineering, product, and marketing partners to bring intelligent solutions into production. The ideal candidate combines deep technical expertise in machine learning, statistical modeling, and AI framework development with strong problem-solving and interpersonal skills, ensuring effective collaboration and measurable impact in a fast-paced environment.
PhD in Computer Science, Statistics, Mathematics, or a related quantitative field with 3 years of relevant experience; or MS with 5 years of experience in applied AI, machine learning, or statistical modeling.\n3 years of programming proficiency with Python for data science and AI (e.g., Pandas, Scikit-learn, NumPy).\n3 years of hands-on experience applying statistical modeling and machine learning algorithms for supervised and unsupervised learning (classification, regression, clustering, etc.).\n3 years of experience working with large-scale data and distributed systems (e.g., Hadoop, Spark).\nWorking familiarity with deep learning algorithms (CNN, RNN, Transformers) and frameworks (TensorFlow, PyTorch).\nWorking familiarity with causal inference models and techniques.\nHands-on experience deploying AI/ML models into a production environment.\nExperience with rapid prototyping, reproduction, and validation of research ideas.\nExperience developing or contributing to AI frameworks, APIs, or internal tools used by other teams.\nExcellent analytical, communication, and collaboration skills across multi-functional teams.\n
Experience with LLM fine-tuning, prompt engineering, or retrieval-augmented generation (RAG).\nFamiliarity with generative AI techniques (e.g., diffusion models).\nKnowledge of computer vision (CV), multimodal models, and video generation/understanding, and their application in video or image analysis.\nExperience building scalable AI/ML systems using cloud platforms (AWS, Google Cloud Platform, Azure) and MLOps tools (e.g., SageMaker, Vertex AI, MLflow).\nExperience applying statistical and ML techniques to analyze user behavior, content usage, conduct feature analytics, and derive product insights.\nHands-on experience with production model monitoring, including tracking data/model drift and running diagnostics.\nProven ability to translate complex research ideas into scalable, production-level AI solutions.\nStrong software engineering practices, including version control, testing, and code review.\nFamiliarity with cross-domain applications of AI/ML (e.g., marketing analytics, personalization, recommendation systems).