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.

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.

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.

This role focuses on building and automating backend tools for Siri's Speech data warehouses, including cataloging, annotation, and making the data queryable via LLM-based chatbots. It involves distributed data engineering at the intersection of speech recognition, NLP, and dialogue management, with the goal of improving training and evaluation of Siri's models.

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.

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.

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.

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.

Data Signals Engineer for Apple Pay focusing on uncovering and prototyping fraud-deterrent signals within the iOS ecosystem, bridging data science, threat analytics, and mobile engineering.

Software Engineer role focused on Triage Intelligence and Debug Engineering within Apple's CoreOS. The role involves improving and maintaining device management systems, collaborating with cross-functional teams, and potentially applying ML/AI techniques to systems problems like crash clustering and log anomaly detection. Familiarity with LLMs and generative AI tooling is preferred.

Software Development Engineer to build highly scalable platforms and services for Apple's Advertising Platforms group. Responsibilities include designing, implementing, and operating distributed and scalable services and data applications, building efficient data pipelines, and applying machine learning techniques to enhance ad targeting and measurement. Requires strong experience in distributed systems, microservices, big data technologies, and cloud platforms.

This role manages a team of crowd managers responsible for overseeing external vendor workforces that produce annotated data for AI/ML models. The focus is on vendor management, production operations, quality oversight, and scaling data annotation processes to meet Apple's AI innovation needs.

Data scientist for Apple Services Digital Marketing team to analyze consumer feedback, optimize marketing and products, and monitor brand relevancy using statistics, ML, digital marketing data, and NLP.

Engineering Manager for App Store's data engineering and analytics team, focusing on compliance-critical data products, pipelines, and analytical outputs. The role involves people leadership, technical guidance, and ensuring operational excellence while partnering with stakeholders to deliver scalable data solutions. Emphasizes AI-native development practices and LLM-augmented workflows across the SDLC, with a strong focus on data quality, governance, and compliance.

Senior Machine Learning & Data Scientist role focused on quantitative analysis of high-dimensional data to improve Apple's software and hardware performance. Responsibilities include producing metrics, models, simulations, and tools, applying statistical analysis to product development problems, and writing production-level code. Requires a strong foundation in statistics, machine learning, and software engineering.

Software Development Engineer focused on data architecture and technical compliance for Apple's commerce platforms. The role involves developing scalable data pipelines and storage solutions, ensuring data quality and observability, and enabling downstream use cases like analytics, reporting, insights, and ML model training/evaluation. Experience with Apache Spark, Flink, Kafka, Iceberg, and object-oriented programming is required.

Software Engineer role focused on developing technologies for Apple's headphone and accessories ecosystem, involving wireless connectivity, ML, and system engineering across iOS, macOS, and tvOS. Requires proficiency in Objective C/Swift and OS concepts, with preferred experience in embedded systems, AIML system modules, and Multimodal ML.

Senior Manager of Quality Assurance for AIML Data Operations at Apple. This role focuses on leading the QA function for data annotation pipelines that feed into AI/ML models. Responsibilities include defining quality standards, managing a team of QA professionals, developing scalable QA processes, and collaborating with Data Science and ML Engineering teams to ensure high-quality labeled data. The role requires a strong understanding of AIML concepts and practical experience in data quality assurance at scale.

The role focuses on building the data and infrastructure ecosystem to support ML development for Siri. It involves end-to-end ownership of services and data products, working closely with ML Modeling, Infrastructure, Software, Hardware, and Operations teams.

This role focuses on biomedical data science within Apple's Health Technologies team, involving the end-to-end development of health and wellness features for future products. Responsibilities include designing and supporting studies, interpreting findings, defining sensor feasibility, developing analysis tools for physiological time series data, and implementing/validating physiological models and algorithms. The role requires collaboration with multidisciplinary teams and a strong understanding of human physiology and multi-sensor systems. Experience with Python for data analysis and visualization is mandatory, with preferred experience in deep learning frameworks, statistical testing, and distributed processing.

This role focuses on building and maintaining scalable ML data infrastructure for creative editing tools, enabling high-quality ML model development. It involves sourcing multimodal datasets, building data pipelines, and supporting model development teams.

This role is for a senior full-stack software engineer focused on delivering high-quality data for LLM training and evaluation. The engineer will build and operate custom internal software solutions, including data collection clients (Swift/web apps), web services, and large-scale distributed data-processing pipelines. The role is crucial for foundational AI development at Apple, contributing to products like Apple Intelligence and Siri.

Engineering Manager for AIML Data Operations at Apple, leading a team that builds software for machine learning data collection and delivery at scale. The role involves technical direction, team leadership, and collaboration with ML researchers and product teams.

Software Development Engineer in Test for the CoreMotion team, focusing on testing and validating CoreMotion features. Responsibilities include building and scaling ML infrastructure, data pipelines, and analytics platforms for model training and evaluation, designing user studies, developing automated test frameworks, and implementing telemetry systems. Requires experience in sensor fusion, signal processing, or machine learning.

Seeking a Data Product Engineering Lead to build and maintain scalable analytical data products and architectures for Apple's Payments & Commerce business. This role involves instrumenting APIs, designing data/ML pipelines, optimizing workflows, and collaborating with partners to deliver actionable insights and decision tools. Requires strong SQL, Scala/Python/Java, data pipeline tools (Spark, Kafka, Airflow), cloud platforms, data warehousing, and data modeling.

This role focuses on analyzing large-scale audio session and device telemetry data to uncover patterns in user behavior and system adjustments, enabling opportunities for predictive and adaptive system improvements. The ideal candidate combines strong data analysis and programming skills with experience working with large-scale datasets. This role will partner with data scientists and engineers to build reliable datasets, develop analytical workflows, and translate telemetry insights into data-driven improvements to device audio experiences.

This role focuses on data operations and capacity planning within an AI/ML context, specifically analyzing annotator performance, task complexity, and data characteristics to optimize data annotation workflows and project scoping. The goal is to ensure high-quality annotated data for unreleased products and AI technology by improving efficiency and quality through data-driven insights and experimentation.

Data Integration Engineer responsible for building and maintaining data pipelines for structured and unstructured data to support AIML model development and deployment. Focuses on data infrastructure, data quality, and integration for sales processes.

This role focuses on human factors research to optimize annotation workflows and analyst tools for machine learning data operations at Apple. The goal is to improve the quality and efficiency of human-annotated data used in AI initiatives, supporting products like Apple Intelligence and Vision Pro.

This role leads a Data Science team focused on AIML Data Operations, establishing health metrics, identifying growth drivers, and recommending optimizations for high-quality annotated data to support unreleased AI products. The focus is on analytics, experimentation, and building cross-functional relationships to drive data-informed decisions.