Machine Learning Engineer - Advanced Analytics

Summary

Imagine what you could do here. At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Itʼs the diversity of those people and their ideas that inspires the innovation that runs through everything we do, from amazing technology to industry-leading environmental efforts. Join Apple, and help us leave the world better than we found it! Do you want to help build some of the largest and most consequential enterprise and customer technology systems in the world? Join Apple’s Information Systems and Technology (IS&T) organization. IS&T is the engine behind everything Apple does for customers and for the people who build for them. It’s Apple’s central nervous system. Supporting 2.5 billion active Apple devices, processing billions of secure transactions, and keeping the technology that defines modern life running flawlessly, IS&T makes the impossible feel effortless.Do you love building solutions to handle global complexity and immense scale? Imagine what you could do here.

Minimum Qualifications

MS in Computer Science, Machine Learning, Applied Math or a related field. 5+ years of industry experience developing Machine Learning Solutions. Hands on experience in Python as programming language. Experience deploying ML solutions to production.

Preferred Qualifications

PhD in Computer Science, Machine Learning, Applied Mathematics, or a related field. 10+ years of experience building and deploying production machine learning systems using Python, C++, TensorFlow, or PyTorch. Deep expertise in machine learning, deep learning, NLP, anomaly detection, large language models, and Generative AI, including modern architectures, retrieval-augmented generation, fine-tuning, prompt engineering, and agentic systems. Experience designing, deploying, and evaluating reliable AI and agentic systems, including tool use, planning, orchestration, human-in-the-loop controls, uncertainty and confidence estimation, robustness, factuality, safety, latency, cost, and end-to-end business outcomes. Experience with scalable data and ML infrastructure, including SQL, Spark, graph databases, cloud platforms, and MLOps. Experience applying reinforcement learning, NLP, and Generative AI to operational, logistics, or supply chain problems. Proven ability to independently deliver high impact AI solutions from concept through production, define meaningful KPIs, and measure business impact. Excellent communication and collaboration skills, with the ability to explain complex technical concepts clearly, work effectively across technical and business teams, and build alignment around AI initiatives.

Description

Sales and Operations Engineering is part of IS&T and drives the technology behind Apple's global operations, sales, and supply chain. The team connects the systems that move products from factory to customer — bridging sales platforms with the operational infrastructure that keeps Apple running at scale and ensuring Apple's technology and business priorities move in lockstep. We are looking for a dynamic, highly motivated, experienced Applied Machine Learning Engineer, with expertise and experience in GenAI, for the Advanced Analytics team to join the IS&T Sales and Ops Engineering. At Apple, the increasing supply chain complexity and scale presents unique challenges. This role is focused on applying GenAI and Machine Learning technologies to solve complex supply chain challenges within an enterprise context. The ideal candidate will have a proven track record of delivering scalable, value-driven ML and GenAI solutions. You will be working with a team of experts responsible for designing and developing solutions leveraging advanced analytics (optimization, ML, simulation and GenAI) from strategic to executional decision-making and with engineering and application teams to integrate these solutions into our existing systems.

Responsibilities

Design and develop advanced machine learning models, several focusing on Generative AI, to address specific supply chain use cases such as demand forecasting, inventory optimization, and logistics planning. Work closely with cross-functional teams, including engineering, data science, and application development, to integrate analytics solutions into enterprise systems. Stay abreast of the latest advancements in machine learning and GenAI, and explore innovative ways to apply these technologies to supply chain challenges. Ensure that analytics solutions are scalable and can be deployed across large, complex enterprise environments. Think big about the arc of development of GenAI over multi year horizon, identity high ROI opportunities that will benefit from this technology and deliver critical projects. Guide partner teams to enable analytics data strategy and infra to support experimentation as well as robust deployment at enterprise scale. Communicate results and insights to partners and senior leaders, technical and non-technical. Influence decision making with leadership. Provide guidance and mentorship to team members and contribute to the overall growth of the analytics team.