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.

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 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.

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.

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 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.

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.

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.

This role focuses on building and maintaining production-grade software systems and ML models for location intelligence using sensor data. It involves the full ML lifecycle from problem formulation to production deployment, with a strong emphasis on signal processing, data infrastructure, and scaling ML to hundreds of millions of devices.

The Machine Learning Test and Automation Engineer will design and build scalable test solutions for validating on-device and distributed Apple Intelligence inference, focusing on correctness, reliability, and performance of the inference runtime software. This role involves developing functional and performance tests within a CI/CD pipeline, partnering with ML engineering and infrastructure teams.

MLOps Engineer role focused on building and optimizing ML infrastructure, ensuring reliability, scalability, and continuous improvement of AI/ML systems in production. Responsibilities include end-to-end quality initiatives, automated pipelines for training, evaluation, deployment, and championing model observability and governance.

This role focuses on building and maintaining the Core ML Runtime for on-device execution of ML models on Apple products. The engineer will work on the ML graph compiler, runtime, and kernels, optimizing model execution for performance, energy efficiency, and thermal management. The role involves developing production-critical system software for implementing ML models on Apple Silicon, with a focus on common compiler optimizations and runtime systems.

Software Engineer role focused on the on-device platform for Siri's attention and invocation features, involving integration of ML models, performance optimization, and system-level architecture for multimodal machine learning solutions across Apple devices.

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.

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.

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.

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.

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.

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 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.

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).

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.

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.

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.