This role is a customer-facing AI/ML consulting engineer focused on positioning Apple's platforms and solutions within enterprise, education, and government sectors. The engineer will provide deep domain expertise on AI/ML solutions (cloud, hybrid, on-device/edge), articulate technical value propositions, advise on scalability, guide integration of AI/ML capabilities, and translate market trends into actionable guidance. The role requires extensive knowledge of Apple's AI/ML stack, including Apple Silicon, Foundation Models, Core ML, and Private Cloud Compute, as well as experience with major cloud AI/ML platforms.

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.

Machine Learning Software Engineer at Apple focusing on spatial awareness and applied perception technologies. The role involves architecting and implementing production software systems for new ML technologies, particularly in computer vision, foundation models, and sensor-based perception. Key responsibilities include developing perception algorithms, optimizing ML training pipelines, fine-tuning vision transformers, and integrating ML with wireless and spatial sensors for iOS devices.

ML Engineer to design, build, and deploy production AI and machine learning solutions for Apple's enterprise, focusing on Generative AI and Data Platforms. The role involves working across the full ML lifecycle, building automated ML pipelines, developing LLM-based solutions (RAG, agents), and integrating ML systems with the enterprise data platform.

This role focuses on architecting and building distributed ML infrastructure for large-scale inference, specifically for powering Apple Intelligence. It involves designing parallelization strategies, optimizing the stack from low-level access to high-level algorithms, and collaborating with hardware architects. The goal is to achieve maximum hardware utilization and minimize latency for real-time user experiences, serving billions of requests daily.

This role focuses on building next-generation AI agents and tools for analyzing wireless connectivity and performance issues in Apple products. It involves developing advanced system evaluation and debugging capabilities using AIML tools, with an emphasis on improving user experience. The role requires expertise in ML frameworks, Gen AI techniques like RAG and Agent Orchestration, and Python scripting, with a strong understanding of wireless protocols.

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.

This role is for a Senior Machine Learning Engineering Manager for Ads Quality at Apple. The primary focus is on leading a team to build and deploy ML models that ensure the quality and integrity of the ad network, with a specific emphasis on using LLMs for relevance, safety, and trustworthiness. The role involves developing and applying advanced ML techniques in a large-scale production environment, working across product, policy, and engineering teams, and analyzing large-scale experiments.

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.

Machine Learning Engineer specializing in Computer Vision for fraud detection within Apple's Strategic Data Solutions team. The role involves building end-to-end solutions, curating datasets, training models, and deploying them into a real-time platform to combat fraud, waste, and abuse.

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.

Machine Learning Research Engineer focused on applying generative AI and ML to create innovative user experiences for hundreds of millions of users across Apple platforms. The role involves the full development cycle from ideation to productization, covering areas like handwriting and text recognition, synthesis, document understanding, and freeform drawing generation, with a focus on computer vision, speech recognition, deep learning, and multimodal LLMs.

ML Applied Scientist role focused on designing, developing, and deploying AI/ML solutions for Apple's services, including LLMs and Agentic AI, to enhance user content discovery. The role involves research, prototyping, production deployment, A/B testing, and collaboration with cross-functional teams.

Machine Learning Engineer at Apple focused on innovating and applying state-of-the-art ML research to complex data problems, specifically for Apple Intelligence. The role involves designing and developing a data generation and curation framework for foundation models, building evaluation pipelines, and exploring new methods for synthetic data creation across vision, text, and audio. The position also emphasizes collaboration with multidisciplinary teams and potentially publishing research.

Senior Machine Learning Engineer focused on Generative AI, responsible for converting high-level goals into concrete requirements, implementing, evaluating, and shipping AI/ML technologies for data quality or user-facing features. Requires experience in building large-scale ML systems, generative AI models, and working with large datasets, with a strong emphasis on shipping production-ready solutions.

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.

Staff Machine Learning Engineer at Apple Ads focused on building ML-driven signal platforms for retrieval, prediction, and relevance. The role involves developing content understanding systems and large-scale infrastructure for near real-time signal updates, enabling privacy-aware decision-making. Key activities include LLM fine-tuning, knowledge graph construction, semantic search, and multimodal representation learning to extract structured intelligence from unstructured data, supporting ad retrieval, creative ranking, and marketplace optimization.

Machine Learning Engineer to build a GenAI system for Apple Audio products, focusing on LLM/LMM development, agents, and RAG applications. Requires experience in ML algorithms, software engineering, and data mining with LLMs/LMMs.

This role focuses on developing, carrying-out, interpreting, and communicating pre- and post-ship evaluations of the safety of Apple Intelligence features, leveraging both human and model-based auto-grading. It also involves researching and developing auto-grading methodology & infrastructure. The role requires creating safety evaluations that uphold Responsible AI values through data sampling, curation, annotation, auto-grading, and analysis. It draws on applied data science, scientific investigation, cross-functional communication, and metrics reporting.

This role focuses on designing, building, and deploying production-grade ML models for abuse detection across multiple modalities (text, image, audio) to protect Apple's ecosystem. It involves owning the full ML lifecycle, driving data strategy, architecting monitoring systems, and collaborating with cross-functional teams to deliver security solutions.

The role focuses on enhancing Siri and Apple products using generative AI and LLMs, with a strong emphasis on applied machine learning and software engineering. Key responsibilities include developing and improving generative experiences, creating evaluation techniques for model responses, and contributing to search relevance. The role also involves mentoring and collaborating with various teams.

Machine Learning Research Scientist for Apple Fitness+ personalization, focusing on developing and deploying state-of-the-art ML models for recommender systems at a global scale, with an emphasis on research and publication.

This role focuses on building and delivering state-of-the-art machine learning models for creative editing tools within Apple's consumer products. Responsibilities include data collection, model architecture design, training custom models, and partnering with cross-functional teams to integrate these features from concept to delivery. The ideal candidate has significant industry experience in computer vision and machine learning, with hands-on experience in building, training, evaluating, and deploying various model types, particularly vision models.

Senior Staff Machine Learning Engineer at Apple Ads, focusing on innovation in ad prediction, quality, and privacy-preserving signals. The role involves building large-scale prediction models, designing privacy-respecting signals, and ensuring ad quality. It requires expertise in deep learning, prediction systems, and privacy-preserving ML, with a strong emphasis on production experience and technical leadership.

Software Engineer focused on building and productizing Generative AI experiences using NLP and ML for Apple platforms. The role involves research, development, and deployment of ML/NLP technologies, with a focus on on-device and private compute cloud applications.

Machine Learning Engineer on the Answers & Knowledge & Information team at Apple, focusing on improving Siri, Spotlight, and Safari search features. This role involves large-scale ML/DL for query understanding and ranking, using word embeddings, deep learning, and online learning on petabytes of data from millions of users. The engineer will design and build infrastructures, perform language and user intent analysis, process web-scale data, and run evaluations.

Senior ML Engineer at Apple focused on building infrastructure and tools for the GenAI lifecycle, from experimentation to production inference, emphasizing observability, reproducibility, and modularity. The role involves contributing to architectural direction and designing for flexibility across future projects, with a strong focus on scalable data/compute infrastructure.

This role focuses on building scalable backend pipelines and services for training data used in Foundation Models and Apple Intelligence features. Responsibilities include converting raw data into training formats, processing and filtering large datasets, and developing APIs for data access. The role also involves large-scale inferences for pre-training and post-training using fine-tuned LLMs.

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.

This role focuses on evaluating, optimizing, and analyzing the performance of ML and multi-modal LLMs. The Data Scientist will develop metrics, conduct failure analysis, process data for evaluation, and implement optimization techniques. They will collaborate with cross-functional teams to integrate models and communicate results. The role requires experience with model evaluation, RAG, and LLM prompt evaluation, with preferred experience in multi-modal foundation models and GenAI frameworks.

Senior Machine Learning Engineer in Finance at Apple, focused on building and extending AI/ML platforms and services for finance organizations. The role involves developing infrastructure for generative AI, agentic workflows, and ML inference, with a strong emphasis on MLOps, observability, and compliance within Finance governance frameworks.

Senior Machine Learning Engineer for Apple Maps, focusing on improving search quality using ML and Generative AI. The role involves developing algorithms for query understanding, intent modeling, ranking, and semantic search, as well as building data pipelines and collaborating with other teams. Experience with LLMs, prompt engineering, and fine-tuning is required, along with strong programming and ML framework knowledge.

Senior Software Development Engineer in Test (SDET) for Apple Services Engineering's AI/ML quality organization, focusing on building developer tools, test frameworks, and libraries for ML pipelines and AI platform services. The role involves defining testing strategies, guiding tool selection, implementing automation, and collaborating with ML Engineers and Data Scientists to ensure quality and governance of ML outputs.

Principal ML Software Engineer for Apple's Creativity Apps team, focusing on building and optimizing on-device ML features for creative editing tools. The role involves architecting efficient execution capabilities, implementing ML algorithms on-device, and collaborating with scientists and designers to deliver performant, ML-based application features, with a strong emphasis on computer vision and iOS platform capabilities.

This role focuses on building AI systems and algorithms to automate crash debugging and root-cause analysis for firmware boot failures in cellular modem chips. The engineer will develop AI-powered engineering tools, LLM APIs, AI agents, and automated workflows, leveraging AI for intelligent automation in silicon bring-up and validation.

This role focuses on enabling Finance teams to transform from manual data preparers to AI-enabled decision partners. The Finance Data Scientist will guide teams through a maturity journey from automation to AI and predictive models, empowering them to leverage capabilities like data automation, BI, advanced analytics, ML, and AI agent creation within a governed framework. Responsibilities include partnering with leaders, developing roadmaps, educating teams on concepts like prompt engineering and agentic AI design, collaborating on solutions like automated workflows and finance-specific AI agents, and championing governance and AI frameworks.

Software Engineer to lead the software development of test systems for Apple products, and integrate AI/ML into daily workflows. This role involves designing and developing AI/ML solutions to improve workflows, team efficiency, and product quality, alongside developing and maintaining test libraries and integrating test systems.

This role focuses on designing and optimizing conversational AI experiences for Apple's online store, aiming to enhance customer engagement and satisfaction. The designer will lead the conceptualization and implementation of chatbot conversations, leveraging technologies like RAG and AI QA tools, and ensuring brand voice consistency. The role requires a blend of creativity, strategic thinking, and technical understanding of how AI models retrieve information and generate responses.

This role focuses on driving manufacturing research and development for iPhone final assembly line automation, integrating AI/ML and smart manufacturing technologies. The engineer will lead automation projects, leverage AI/ML and LLMs to optimize processes, and develop data-driven methods. Responsibilities include spearheading AI/ML adoption, leading core engineering projects, developing intelligent testing methods, conducting advanced data analysis, and mentoring teams on AI-augmented manufacturing.

The Senior Product Manager will define and deliver AI-powered solutions for live streaming moderation and VOC analytics, owning the product lifecycle and driving execution for AI systems that protect brand integrity and transform customer feedback into actionable business intelligence. This role requires a strong technical understanding of AI, familiarity with APAC social commerce, and the ability to translate business problems into product requirements, working with engineering teams to ship products that balance automation with human oversight.

This role focuses on applying Generative AI and machine learning techniques to financial planning and analysis (FP&A) within the APAC region. The analyst will own core FP&A responsibilities, build intelligent data products to transform financial workflows, and leverage LLMs/GenAI frameworks for finance use cases. The role requires strong data fluency, experience with modern data science tools, and the ability to work with incomplete data to generate insights.

Software Engineer to design and build intelligent tooling, automation, and AI-powered workflows using AI agents to improve engineering productivity and software quality across Apple's media technology stack. This role involves prototyping and productizing systems that leverage agentic coding workflows, AI-driven code analysis, and automation pipelines to streamline development processes, accelerate issue resolution, and enhance code quality at scale.

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.

Develop and train deep learning models for image restoration and fusion applications to improve Apple's camera imaging pipeline, contributing to consumer products.

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.

This role focuses on building and deploying machine learning models to create intelligent search experiences, aiming to improve search relevance, ranking, and user satisfaction within Apple's consumer products. The engineer will be responsible for the end-to-end lifecycle of these models, from research to production.

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.

Engineering Program Manager for AI/ML and Generative AI features within Apple Media Products. This role involves driving the end-to-end delivery of large-scale, cross-functional ML/GenAI programs, from strategic planning and execution to managing timelines, resources, and cross-functional collaboration. The role requires understanding the full ML lifecycle and mitigating technical challenges to ensure successful product launches.