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. The people here at Apple don’t just create products — they create the kind of wonder that’s revolutionized entire industries. It’s 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. ## Description With the explosive growth of Apple products we are creating new opportunities for individuals to work on the most exciting new technologies at Apple. We are seeking a machine learning/operations research engineer to apply advanced mathematical modeling, statistical analysis, and optimization algorithms to solve complex manufacturing challenges. Machine learning/operations research engineers on our team directly impact our factory throughput, supply chain strategies, and cost-reduction initiatives by transforming raw operational data into actionable, data-driven decisions. ## Responsibilities Production Optimization: Develop and implement mathematical models for optimization of capacity, yield, cycle times, costs, throughput, shop-floor layout usage, dynamic scheduling, and other factory and supply chain metrics. Simulation Modeling: Build and maintain mathematical models for simulation to act as a "digital twin" of our assembly lines, identifying bottlenecks and testing "what-if" capacity scenarios. Data fusion and analytics: Develop and implement data fusion techniques to integrate different operational data sources and generate actionable insights for manufacturing intelligence. ## Minimum qualifications Master’s degree or PhD in Operations Research, Industrial Engineering, Management Science, Applied Mathematics, or related field. Proficiency with solvers and modeling languages such as Gurobi, CPLEX, CP-SAT, Pyomo, and GAMS. Hands-on experience with simulation platforms like Arena, FlexSim, SimPy, and AnyLogic. Experience with machine learning platforms such as PyTorch and Scikit-learn. Excellent communication and presentation skills; ability to explain complex statistical and mathematical theories to non-technical stakeholders in simple, universal language. ## Preferred qualifications Proven experience in GenAI application building with agents and agentic workflows. Experience with LLM and LMM development and fine-tuning is a major plus. Proficiency in using cutting-edge GenAI tools, i.e. Claude Code, Roo Code, etc. Familiarity with distributed computing, cloud infrastructure, and orchestration tools, such as Kubernetes, Apache Airflow (DAG), Docker, Conductor, Ray for LLM training and inference at scale is a plus.