Imagine what you could do here. At Apple, great ideas have a way of becoming great products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Are you an enthusiastic Machine Learning Engineer eager to apply your expertise in a fast-paced, innovative tech environment? Join our Global Sourcing & Supply Management (GSSM) Solutions team as a key player in revolutionizing our supply chain. As a Machine Learning Engineer on our core AI/ML team, you will design and build GenAI-powered features and workflows leveraging LLMs and modern AI techniques. You will collaborate closely with business stakeholders, product teams, and data engineers to translate complex challenges into practical AI/ML solutions and effectively communicate insights to senior management. Your work will empower data-driven decision-making, optimize workflows, and drive measurable impact across the supply chain. If you thrive in a collaborative environment, are passionate about applying AI/ML to solve real-world business problems, and are excited to work with cutting-edge GenAI technologies, we want to hear from you!

Description

- Partner with business and product teams to identify high-impact opportunities and translate ambiguous requirements into GenAI-powered features and workflows delivered through a shared AI platform and embedded across products - Design, build, and own end-to-end GenAI capabilities that support both a centralized AI platform and product teams, covering all aspects from prompt and tool design to agent orchestration, retrieval strategies, model selection, and system evaluation - Develop reliable, scalable, and cost-aware GenAI features in collaboration with platform, data, and application engineering teams, ensuring strong performance, observability, and maintainability in production environments - Establish evaluation and monitoring strategies for GenAI-driven features, focusing on output quality, correctness, safety, and business relevance through offline benchmarks, automated checks, and human-in-the-loop review - Develop Text-to-SQL and structured reasoning capabilities that enable natural-language interaction with structured data, ensuring accuracy, security, and alignment with business semantics - Leverage agentic AI patterns (multi-step reasoning, tool use, planning, memory, feedback loops) to support complex workflows, while establishing guardrails for reliable and predictable behavior - Communicate trade-offs, system behavior, and limitations clearly to technical and non-technical stakeholders, enabling informed product and business decisions - Continuously research, prototype, and operationalize emerging GenAI techniques to improve platform capabilities and accelerate adoption across teams

Minimum Qualifications

Bachelors degree PhD/MS in Computer Science, Statistics, Applied Math or a related field 5+ years of industry experience

Preferred Qualifications

Strong problem-solving skills and the ability to tackle ambiguous, real-world challenges, along with clear communication and collaboration skills Experience with modern deep learning frameworks, such as PyTorch or TensorFlow Hands-on experience working with transformer-based models, including large language models (e.g., GPT style models or BERT-like architectures) Practical experience leveraging LLMs or GenAI models via APIs to create reliable and user-facing features or workflows Familiarity with common GenAI tools and frameworks, such as LangChain or similar, with the ability to learn and adapt as the ecosystem evolves Solid understanding of foundational ML concepts including supervised, unsupervised and reinforcement learning Solid understanding of core machine learning concepts, including supervised and unsupervised learning; exposure to reinforcement learning is a plus Experience with model deployment pipelines and serving GenAI models in production Experience applying modern ML or GenAI techniques in production workflows, including tasks such as Retrieval-Augmented Generation (RAG), structured reasoning, or prompt-based system design Experience working in Supply Chain, Operations, or a related field Ability to operate independently and lead without authority

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