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.

Senior Software Architect for Siri Attention and Invocation team, focusing on designing and implementing software systems for natural and conversational interactions with virtual assistants using AI/ML technologies, including LLMs and conversational AI. The role involves architecting ML-driven user experiences, integrating with sensory and system software, and optimizing on-device runtime performance.

Staff Machine Learning Performance Engineer for Siri, focusing on optimizing LLM and ML model inference stacks for performance and efficiency, involving on-device vs. server model decisions and collaboration with hardware/software teams.

This role is for a Sr. Software Engineer on the Siri Speech team at Apple, focusing on delivering natural language capabilities using ML. The responsibilities include productizing new models for Apple hardware, designing inference systems, building cloud services, and optimizing performance. The role requires experience in iOS development, generative AI agents for coding, and production model inference, with familiarity with large language models preferred.

Machine Learning Engineer at Apple to build next-generation features using multi-modal sensing, focusing on motion sensing and interactive technologies. Responsibilities include designing and implementing models & algorithms, optimizing for power, memory, and performance, developing data pipelines, and deploying efficient, low-power models for customer-facing products.

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.

This role is for a Creative Technologist in Apple's Marcom team, focusing on R&D and prototyping of innovative experiences using emerging technologies like generative AI, LLMs, diffusion models, and agents. The role involves coding, research, experimentation, and collaboration to integrate these technologies into internal workflows and customer-facing 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.

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.

Software Engineer on the Applied Machine Learning Team to architect and orchestrate high-performance, scalable enterprise platforms for Data, ML, and inferencing. Focus on ensuring availability, performance, and low latency for high-throughput applications. Manage diverse workloads across ML/Data/Inference platforms and evaluate new technologies.

This role focuses on building and shipping the ML platform and tools that enable product teams across Apple to develop and deploy machine learning solutions. The engineer will be responsible for the infrastructure, tools, and developing ML solutions, with a strong emphasis on scalability, reliability, and ease of use.

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.

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.

Quality Engineer for Machine Learning in Apple's Creative Music Apps team, focusing on testing ML models and DSP algorithms for audio features on macOS, iOS & iPadOS. Responsibilities include stress-testing for regressions, designing test strategies, developing automated tests, and collaborating with ML engineers on quality metrics.

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.

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.

This role focuses on building and scaling automated insight pipelines for the sales organization, developing ML models for opportunity detection and performance diagnosis, and embedding these insights into AI agents, dashboards, and GenAI tools. The role involves end-to-end insight development, including data preparation, statistical analysis, LLM prompt engineering, and deploying ML models for forecasting, anomaly detection, attribution, and causal inference. It also includes building RCA and recommendation engines, analyzing agent interactions, implementing LLM evaluation pipelines, and supporting experimentation. The role requires partnering with AI engineers and PMs, acting as a data translator, influencing upstream data model design, and driving KPI definitions.

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.

Manager for a Machine Learning Engineering team focused on manufacturing applications at Apple. The role involves leading a team of ML and MLOps engineers to build, deploy, and scale AI-driven systems for the product lifecycle, from prototype to announcement. Requires technical expertise in AI, leadership skills, and experience with MLOps, Computer Vision, and scaling ML systems.

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.

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.