Apple

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.

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.

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.

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.

Software Engineer role focused on developing and delivering AI/ML solutions for Apple's hardware product line, including agentic systems and RAG architectures, with responsibilities spanning training, evaluation, and inference pipelines.

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.

Seeking an experienced AI/ML Security Engineer to design and implement security frameworks for AI/ML pipelines, conduct security assessments, develop automated testing and monitoring, lead incident response, and establish secure MLOps practices. The role also involves strategic business leadership, translating risks, developing roadmaps, and collaborating with cross-functional teams.

Senior Software Engineer focused on building AI-enabled observability solutions for Apple's AI, Search, and Knowledge Platform. The role involves designing and developing user-facing observability features for AIML products and infrastructure, leveraging AI/ML for AIOps, and working with LLMs, ML frameworks, and agentic AI concepts. The position emphasizes building scalable, cloud-native distributed systems and microservices, with a focus on anomaly detection, incident detection, and root-cause analysis within AI observability.

This role focuses on building and scaling cloud-native ML infrastructure, specifically platforms for ML training and inference. It involves designing and implementing agentic workflows, Kubernetes-based control planes, and infrastructure servers to manage these systems at a large scale. The role emphasizes open-source contributions and expertise in Kubernetes and Crossplane.

Staff Software Engineer to advance Manufacturing Systems and Operations using LLMs, from prototyping to production and scaling. This role involves working with various organizations to develop impactful solutions, some standalone and others integrated into existing systems.

Internship role focused on software engineering and natural language processing within the Siri & Information Intelligence team at Apple. The intern will collaborate with experts, receive mentorship, and work on developing technologies and experiences for the next generation of Siri products.

Senior Machine Learning Platform Engineer at Apple, focused on building and scaling the AI, Search & Knowledge platform. This role involves creating seamless integrations between ML frameworks and the platform, designing Python SDKs and APIs, and building backend services to support model management and serving infrastructure. The goal is to enable ML practitioners to focus on innovation by abstracting infrastructure complexity.

Machine Learning Engineer role focused on improving search quality for Apple Maps using Generative AI and LLMs at scale. Requires experience delivering ML-driven products and strong programming skills.