Data Engineer role focused on building and maintaining scalable data infrastructure for analytics, machine learning, and AI-driven decision-making within Operations. The role involves designing data pipelines, building data models, implementing data observability, and collaborating with data science and ML engineering teams. It also emphasizes leveraging AI-assisted tools for development and researching emerging GenAI data tooling.

Software Engineer focused on data collection, processing, and ML training data curation for on-device sensors and wireless technologies. The role involves building tools for data analysis, creating datasets for LLM benchmarking, and developing frameworks for evaluating generative AI output quality and reliability. It also includes designing data pipelines for sensor data and collaborating with teams to create intelligent experiences.

Software Engineer focused on building and managing internal security services that leverage AI technologies to identify and remediate security risks within the software development lifecycle. The role involves assessing AI-specific security risks and implementing controls, with a preference for experience in LLM-powered solutions.

Senior Engineering Manager to lead a team building foundational systems and services for Apple's AI and Data governance platform. The role focuses on Big Data management, ML infrastructure, and Generative AI, enabling efficient and scalable model development while ensuring regulatory compliance. It involves leading efforts in data lake governance and GenAI infrastructure across the entire model development lifecycle, influencing ML practitioners and product development.

Applied Scientist role at Apple focusing on causal inference, statistics, and machine learning to optimize marketing channels for consumer services. The role involves designing, developing, and deploying solutions using observational testing, counterfactual modeling, and lifetime value estimation, working with large-scale, privacy-focused datasets. Key responsibilities include engineering end-to-end causal inference products, analyzing data for automation and modeling opportunities, collaborating with cross-functional teams, and staying updated on research advancements.

This role focuses on building AI-assisted systems to analyze CI build and test failures, transforming raw data into actionable insights for OS engineers. The engineer will design and implement AI-powered workflows for failure summarization, pattern identification, and distinguishing signal from noise, integrating AI capabilities into developer tools to accelerate the software development process.

Product Manager for Generative AI within Apple's Ai & Data Platforms team, focusing on building and scaling GenAI capabilities for enterprise use cases like knowledge discovery, conversational AI, and workflow automation. The role involves defining product strategy, working closely with engineering and data science, and ensuring AI is trustworthy, governable, and reliable at scale. Requires a technical background, ability to prototype, and experience shipping AI/ML products.

This role leads the product engineering and building efforts for AI-driven tools within a legal tech ecosystem, focusing on rapid development, prototyping, and delivering legal-contextualized AI applications and features embedded into existing legal workflows. The role involves managing a team, building upon internal AI platforms, and bridging the gap between AI capabilities and business needs.

Machine Learning Engineer role focused on applying ML and optimization to supply chain and logistics problems for Apple's Fulfillment Operations team. The role involves designing, building, deploying, and maintaining end-to-end data pipelines and models to improve operational efficiency and security, with a focus on productionizing and owning the stack.

Machine Learning Engineer focused on Trust and Safety for communication apps, protecting against spam and abuse. The role involves building ML tooling for the entire ML lifecycle, from data ETL and model training to evaluation and deployment, with a strong emphasis on anti-fraud and anomaly detection, particularly in the context of LLMs. The goal is to deliver reusable tooling and integrate with existing systems to enhance customer safety and mitigate attack vectors.

This role is for an AI Solutions Engineer in Apple's Innovation Lab, focusing on helping enterprise customers build applications on Apple platforms using AI and Spatial Computing. The role involves consulting, architecting, building demonstrations, and evangelizing best practices for AI-powered enterprise applications. Requires expertise in LLMs, RAG, traditional ML, and Apple platform development.

This role focuses on applying AI/ML to Wi-Fi software for Apple products, optimizing performance, predicting congestion, and enhancing connection reliability. The engineer will design and implement ML models, apply AI to internal tools, analyze network data, and optimize models for on-device inference. The role involves collaboration with hardware and software teams, and a strong understanding of networking protocols and wireless standards is required.

Senior Software Engineer for Apple's Applied Sensing & Health team, focusing on delivering Health and Fitness features for Apple products. The role involves architecting and implementing C++ systems for sensor data processing, translating ML research into production algorithms, and maintaining system software with real-time constraints. Requires strong C++ skills, Python for ML, and understanding of ML pipelines.

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.

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.

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.

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.

Develops software for next-generation fitness, safety, and health experiences by transforming multi-modal sensor data into insights using advanced machine learning and foundation models, impacting millions of Apple Watch, iPhone, and AirPods users.

Senior Machine Learning Engineer to join the Predictions group, focusing on building core ML models for ad predictions and monetization across Apple's App Store and News platforms. The role involves designing and implementing ML models for user interaction, CTR, and CVR prediction, developing retrieval algorithms, and contributing to modeling areas like deep neural networks, contextual bandits, multi-task learning, and LLM-based ranking signals. It also requires working with large-scale datasets, collaborating with cross-functional teams, and running experiments. Experience with ad tech, recommender systems, or web-scale search/retrieval is preferred, along with deep expertise in neural network architectures and training pipelines.

This role focuses on building APIs for AI models within Apple's Swift Frameworks team. The engineer will implement features, design APIs, and be involved in the full development lifecycle from research to testing and documentation. Collaboration with ML researchers and systems engineers is key.

Engineering Manager to lead a team building foundational ML systems for personalization on Apple's consumer platforms. The role involves managing ML engineers, defining technical strategy for distributed feature access and pipelines, and ensuring data quality and privacy compliance at petabyte scale. It bridges data engineering, ML systems, and privacy, focusing on the data layer that powers intelligent experiences.

Machine Learning Engineer focused on building foundational capabilities for personalization experiences across Apple's consumer products. The role involves engineering large-scale feature pipelines, architecting training data systems, and optimizing for privacy and scale, bridging edge devices and cloud backends. This is a senior-level role requiring expertise in distributed data processing and serving layers.

Machine Learning Engineer focused on the data and evaluation lifecycle for production models in computer vision and audio. Responsibilities include scaling data pipelines, ensuring data quality, performing failure analysis, implementing data augmentation, and designing evaluation metrics for models. The role bridges hardware, software, and modeling for efficient inference.

This role focuses on building the backbone of an AI-powered developer platform that uses AI agents and autonomous workflows to support app development within Apple. The engineer will design and maintain backend services and orchestration systems for multi-agent AI workflows, integrate LLMs into developer tools, and work on cloud deployments and MLOps.

Senior Software Engineer to design and build intelligent tooling and automation using AI-driven agents to improve engineering productivity and content operations. The role involves prototyping and productionizing systems that leverage agentic coding workflows and AI-driven content processing to streamline development and publishing processes.

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.

The Senior Manager of AI Data Operations will lead an organization of internal domain experts to build scalable annotation programs that balance accuracy and speed while maintaining quality standards. This role partners with Engineering, Data Science, and Programs to translate evolving system requirements into robust audit pipelines, tooling strategies, and measurable performance lift. Responsibilities include owning end-to-end data annotation and system audit operations, designing quality frameworks, operationalizing auditing for training, evaluation, and red-teaming, driving tooling evolution and automation strategy, and developing data augmentation strategies.

Senior Machine Learning Engineer at Apple focused on building and optimizing the core auction system for their advertising platform. The role involves applying machine learning and auction theory to drive optimal outcomes for advertisers and users, working with large-scale systems and production code.

Senior Machine Learning Engineer at Apple focused on Ads Bidding & Pacing. The role involves designing and building scalable ML solutions for ad optimization, budget, and bid strategies to improve campaign performance and user experience. Requires experience in ML, quantitative optimization, production code development, and large-scale experimentation, ideally within the ads, recommendations, or search relevance domains.

Senior Machine Learning Engineer at Apple Ads focusing on building and optimizing core ML models for ad predictions, user interaction, click-through rate, and conversion rate. The role involves deep learning, information retrieval, large-scale modeling, and exploring LLMs and Reinforcement Learning for ad prediction systems across Apple's App Store and News platforms.

Research Scientist role focused on developing and deploying ML technologies for understanding, interacting with, evaluating, and generating user interfaces across Apple platforms. The work integrates into various Apple products and services, with a focus on shipping both internal tools and external products.

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.

Staff Data Science Engineer for Siri's Attention and Invocation team, focusing on making Siri and Apple products more intelligent. The role involves leading ML solution development, establishing best practices for model validation and monitoring, driving technical decisions for ML/AI initiatives, and collaborating with stakeholders to define requirements and shape the data science roadmap.

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.

Staff AI Software Engineer for Siri Core Modeling at Apple, focusing on the on-device software platform for Apple Intelligence. The role involves designing and developing features, implementing performant code, identifying architecture changes, optimizing performance, and developing software that interfaces with LLMs and on-device intelligence to enhance Siri capabilities. The position also emphasizes leveraging LLMs and AI assistants for engineering productivity.

Staff Machine Learning Engineer for Apple Ads Platform, focusing on building and deploying ML systems and data pipelines to enhance advertiser trust and invalid traffic protections. The role involves defining an innovation roadmap, implementing robust CI/CD, feature stores, and streaming infrastructure, running A/B experiments, and optimizing performance for product quality, user experience, latency, and cost.

Develop and extend software features for iPhone camera, focusing on computer vision, image processing, and machine learning to deliver high-quality photos and videos. This role involves prototyping algorithms, working cross-functionally, and implementing solutions for new camera capabilities across Apple devices.

Staff Machine Learning Engineer on the ML Platform team at Apple Ads, responsible for building and scaling shared ML platforms, frameworks, and services. The role focuses on enabling other teams to build and scale ML features, models, and applications, with a deep understanding of the ML lifecycle, deep learning architectures, and experience applying ML at scale in ads or recommender systems. Experience with privacy-preserving ML and AI/ML tooling is preferred.

Engineering Manager to lead a team building foundational ML platforms and frameworks for training, evaluation, and deployment of models across Apple services. Focus on scalability, reliability, and developer experience for ML practitioners.

The Applied ML Science Manager will lead a team focused on Causal Inference and AIML solutions for Apple's services, impacting business decisions and marketing optimization. The role involves developing and deploying cutting-edge solutions, defining technical vision, and fostering a collaborative team culture. Responsibilities include leading scientists and engineers, overseeing technical strategy, managing projects, and collaborating with cross-functional teams. Requires a Master's degree, leadership experience, and expertise in Causal Inference techniques, ML, and programming languages.

Senior Software Engineer on the Siri Speech team at Apple, focusing on designing and developing next-generation Speech-to-Text APIs. This role involves integrating ML and generative AI capabilities into scalable APIs, collaborating with ML research scientists to productionize speech models, and championing software engineering best practices.

Software Engineer role focused on building tools and systems for the automatic evaluation of Apple's AI products, specifically using LLM-as-judge and related technologies to improve the quality and efficiency of these evaluations. The role involves designing and developing frameworks, pipelines, and tools for AI model development, deployment, and measurement, directly impacting product launch decisions.

Machine Learning Engineer focused on localizing and scaling LLMs and NLP models for Apple's international customers. The role involves end-to-end model development, including training, experimentation, evaluation, and deployment, with a focus on data-driven quality programs and building tools for software translation.

This role focuses on operationalizing machine learning models for recommendations and personalization systems at Apple. The primary responsibility is building and managing real-time and batch inference pipelines, optimizing system performance, and driving experimentation. The role bridges research and production by developing infrastructure, tooling, and monitoring for shipping ML-driven features. It involves partnering with ML researchers, designing inference services, building data pipelines, developing deployment and evaluation tooling, leading A/B testing, and collaborating on observability and system reliability. While the core is inference (L3), the mention of agents and LLMs in preferred qualifications suggests a secondary focus on agentic systems (L4).

Machine Learning Engineer to join the Ads Relevance and Quality team, responsible for building intelligent systems to evaluate ad relevance, detect low-quality or offensive content, and optimize user satisfaction using applied ML, NLP, and content quality techniques.

Manager for Responsible Development & Safety in Apple Services Engineering, focusing on shaping policies, evaluating AI models and applications, and ensuring safe deployment of user-facing features. The role involves leading a team, collaborating with various cross-functional teams, and developing evaluation processes for AI/ML models.

This role focuses on designing and implementing evaluation frameworks for AI/ML systems, specifically for Apple's consumer-facing products. The Data Scientist will work with large datasets, develop methodologies for assessing product quality, and partner with engineering teams to improve user experience and guide feature development. The role involves building evaluation datasets, human-in-the-loop systems, and translating insights into actionable recommendations.

This role focuses on applying machine learning and computer vision to industrial problems in manufacturing, specifically for inspection equipment. The engineer will develop and deploy ML models to judge millions of units daily, identify patterns and anomalies in data, and build toolkits for applying ML and statistical tools. Responsibilities include data analysis, software development in Python/Java/C++, and working with inspection systems. The role requires strong ML/CV experience in industrial settings, proficiency in Python and ML algorithms, and experience with image processing. It involves developing solutions from inception to production, with a focus on shipping AI-powered products for quality control in manufacturing.