Senior GenAI Engineer, Apple Cloud AI

The Apple Cloud AI team powers data and AI capabilities that fuel Music, TV, App Store, Maps, iCloud and many other beloved products in the Apple ecosystem. Our mission is to provide engineers and data scientists with an innovative, reliable, secure, and user-friendly platform for Agentic and Generative AI development and deployment at scale. As a Senior GenAI Engineer on the Apple Cloud AI group’s Agent Platform team, you’ll play a critical role in shaping and scaling the platform that powers AI application development across Apple. You’ll architect and build core capabilities such as agentic harness, agent orchestration, model selection, building services for agent context and knowledge management, and evaluation tooling enabling teams to rapidly create, iterate, and deploy GenAI applications. Your work will also support advanced initiatives like multi-agentic systems, long horizon agents, model fine-tuning and reinforcement learning workflows, and inference optimization, driving a seamless and flexible developer experience for users of Apple Cloud AI. Join us and be a part of the Data and AI Platform journey at Apple, where your contributions will impact Apple's customers and countless teams within Apple.

Description

Join Apple’s Cloud AI as a Senior GenAI Engineer, where you'll be at the forefront of delivering transformative AI experiences across Apple products, including Apple Music, TV, Sports and many more. You will play a pivotal role in defining and driving the roadmap for Apple Cloud GenAI and Agentic Platform component, which powers the development, evaluation, and deployment of AI agents and applications. This includes building foundational capabilities such as agent orchestration, model configuration, prompt tooling, RAG integration, and inference optimization enabling teams across Apple to rapidly create scalable, context-aware GenAI solutions. You will collaborate closely with cross-functional teams of innovative software engineers, product managers, and engineering leaders to continuously evolve the platform for performance, flexibility, and ease of use.

Minimum Qualifications

4+ years of machine learning and software development experience

1+ years of experience as a technical lead, guiding teams through complex design decisions and setting high benchmarks for code quality, performance, and scalability

Hands-on experience designing and building GenAI platforms that allow users to create, configure, and deploy AI Agents supporting features like agent orchestration, prompt engineering, RAG integration, and model selection

Experience building AI agents capable of complex multi-step reasoning and tool usage, with a focus on reliability, traceability, and composability

Experience building and deploying Agent Harnesses, including integrations with Agent Memory and Context, MCP and tools, and runtime secure environments (Agent Sandboxes)

In-depth understanding of security primitives for AI Agents and Harnesses including policy enforcement, MCP/tools access control, runtime security, drift and hallucination detection for long horizon agents.

In-depth understanding of large language models (LLMs) and their application in AI-driven solutions, including inferencing, embedding, and knowledge base integration (RAG) for improved data retrieval and contextualization

Knowledge of LLM inference optimization techniques, including prompt tuning, caching, quantization, and latency reduction across different model families

Strong programming skills in Python, Java, or similar languages, with an emphasis on AI/ML systems development and platform engineering

Demonstrated ability to work cross-functionally and influence product development through a combination of technical leadership and user-centered thinking

Passion for operational excellence, automation, and delivering scalable, developer-friendly AI infrastructure

B.S, M.S. or PhD Degree in Computer Science/Engineering, or equivalent work experience

Preferred Qualifications

Expertise in 3rd party Cloud Environments (Google Cloud and AWS)

Hands on experience in using Kubernetes as orchestration layer

Proven experience in customizing foundation models to improve task-specific performance and domain alignment