Machine LearningOperations Research Engineer

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 theres no telling what you could accomplish. nnThe people here at Apple dont just create products they create the kind of wonder thats revolutionized entire industries. Its 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.

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.

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 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 fusion and analytics: Develop and implement data fusion techniques to integrate different operational data sources and generate actionable insights for manufacturing intelligence.

Masters degree or PhD in Operations Research Industrial Engineering Management Science Applied Mathematics or related with solvers and modeling languages such as Gurobi CPLEX CP-SAT Pyomo and -on experience with simulation platforms like Arena FlexSim SimPy and with machine learning platforms such as PyTorch and communication and presentation skills; ability to explain complex statistical and mathematical theories to non-technical stakeholders in simple universal language.

Proven experience in GenAI application building with agents and agentic workflows. Experience with LLM and LMM development and fine-tuning is a major in using cutting-edge GenAI tools i.e. Claude Code Roo Code 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.