As a Worldwide Consulting Engineer focused on AI/ML within Apple's Enterprise, Education & Government sales organization, you will be a critical part of a global team responsible for ensuring the technical integrity and strategic positioning of Apple platforms and solutions in the rapidly evolving AI/ML landscape. You will be a strategic advisor, technical visionary, and subject matter expert, shaping the future of Apple's presence in business and education organizations worldwide.\n\nYou will provide deep domain expertise across cloud, hybrid, and on-device/edge AI/ML solutions, demonstrating Apple's unique advantages in end-user compute and mobility - including Apple Silicon, the Neural Engine, Apple Foundation Models, and Private Cloud Compute - and how they extend to critical areas such as industry trends, use case development, and the transformative impact of AI/ML on work and learning.\nYou will be instrumental in identifying and surfacing technical barriers to broader adoption of Apple technologies within Business and Education environments, effectively communicating customer needs and insights directly to Software, Hardware, Services, and Product teams. You will be a key bridge between customers and Apple's engineering teams, ensuring that market trends are reflected in Apple's product roadmap.

In this role, you will: Empower and educate global Field Sales Engineers, customers, and partners on Apple's product advantages - Mac, iOS, iPadOS, Apple Silicon - across cloud-based, hybrid, on-device, and edge AI/ML architectures, with fluency in where each deployment model is most appropriate. Develop and articulate compelling technical value propositions for Apple platforms across enterprise, education, and government sectors, with particular depth around Apple Foundation Models (AFM), on-device inference, and Apple Intelligence features. Collaborate with cross-functional teams - including Product Marketing, Business Markets, Engineering, and Sales Leadership - to define and refine go-to-market strategies and AI/ML solution offerings leveraging Apple technology. Engage directly with strategic customers and partners in deep technical discussions, understanding their AI/ML initiatives and positioning Apple's unique capabilities - including Private Cloud Compute (PCC) privacy guarantees - to meet their needs and drive innovation. Advise on scalability strategies for on-device and edge AI/ML solutions, determining when a single node is sufficient versus when a cluster approach is necessary, including hardware selection and model serving architecture for sovereign AI deployments. Guide enterprise customers and developers on integrating AI/ML capabilities into applications using approaches such as Model Context Protocol (MCP) and App Intents, and how to evaluate tradeoffs between them. Monitor and analyze emerging AI/ML trends, technologies, and industry shifts - translating insights into actionable guidance for field teams and leadership, with a continuous learning mindset. Lead key customer and partner engagements including executive briefings, workshops, proof-of-concepts, and technical presentations showcasing Apple's leadership in the AI/ML space. Contribute to technical resources, training modules, and best practices that scale AI/ML knowledge and capabilities across the global sales organization. Drive thought leadership internally and externally on the transformative impact of AI/ML on work and learning, with a focus on Apple's contributions and advantages. Note: This role requires periodic domestic and international travel.

7+ years of experience in a highly technical, customer-facing AI/ML role - such as a Solutions Engineer or Solutions Architect at a major cloud provider; a Field CTO, Technical Evangelist, or Field Engineer at an AI platform or foundation model company; or a Principal Technical Consultant at an enterprise technology organization. Deep and demonstrable expertise in AI/ML concepts and applications, including: Machine learning algorithms (supervised, unsupervised, reinforcement learning) Deep learning architectures (CNNs, RNNs, Transformers, diffusion models) MLOps principles, data science methodologies, and model deployment strategies Hands-on experience with major cloud AI/ML platforms (e.g., AWS SageMaker, Azure ML, Google AI Platform) Deep, hands-on knowledge of Apple's on-device AI/ML stack, including: Understanding of Apple Silicon architecture and its benefits for on-device inference and other AI/ML workflows Apple Foundation Models (AFM) and their capabilities and appropriate use cases Core ML, Core ML Tools, and related on-device inference frameworks Private Cloud Compute (PCC) architecture and privacy guarantees Strong ability to articulate the distinction between on-device inference, Private Cloud Compute, and third-party cloud-hosted models - and when each is the right architecture for a given use case. Demonstrated ability to engage directly with large organizations and partners on strategic AI/ML opportunities, advocating for Apple's platform advantages in real-world use cases.

Experience collaborating with product management, software engineering, and hardware engineering teams to influence platform direction and roadmap priorities. Hands-on experience with MLX for training, fine-tuning, and running inference with LLMs and other model types on Mac hardware, including awareness of model sizing tradeoffs for on-device deployment. Demonstrated experience optimizing and deploying machine learning models for on-device inference on Apple Silicon, leveraging Core ML Tools, Metal Performance Shaders, or other optimization techniques. Practical experience designing or advising on sovereign/private AI cluster deployments using Apple hardware - including Mac selection and configuration, model serving, and remote client access. Experience building AI/ML capabilities into applications using Model Context Protocol (MCP) - including building MCP servers - and/or App Intents for Siri and Apple Intelligence integration. Practical experience with generative AI models beyond LLMs (e.g., diffusion models, GANs) and their application in enterprise, education, or government contexts. Familiarity with Apple developer tooling relevant to AI/ML, including Xcode (including agentic coding features) and the Foundation Models framework. Hands-on experience with scripting or automation (Swift, Python, Bash, or similar) for testing, deployment, or performance analysis.