Machine Learning Engineer - Global Sourcing & Supply Management (GSSM) Solutions

Summary
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 and help drive data-informed decisions across our supply chain.

Description
As a Machine Learning Engineer on our core AI/ML team, you will analyze complex datasets, develop predictive and statistical models, and deliver insights that inform strategy and product direction. You will collaborate closely with business stakeholders, product teams, and data engineers to translate ambiguous questions into structured analyses and practical data-driven solutions. Your work will support experimentation, forecasting, optimization and measurable business impact across the supply chain.

Responsibilities

• Design, develop, and deploy machine learning models and AI systems for forecasting, optimization, decision-making, and intelligent workflow automation
• Build production-ready ML systems, including model inference services, APIs, data pipelines, and scalable ML applications
• Develop and evaluate ML models using appropriate metrics, validation strategies, experimentation frameworks, and offline evaluation
• Build analytical and AI-driven prototypes, including GenAI, LLM, structured reasoning, and Text-to-SQL workflows, and transition successful approaches into production
• Design and implement scalable model inference services capable of supporting high-volume workloads with strong reliability and performance
• Develop MLOps pipelines for model deployment, monitoring, evaluation, and continuous improvement
• Monitor model performance, system reliability, latency, and resource utilization in production; identify and resolve performance issues
• Partner with business and product teams to identify high-impact AI/ML opportunities and translate ambiguous requirements into scalable ML problem statements and measurable outcomes
• Optimize models and ML systems for speed, scalability, efficiency, and cost
• Collaborate closely with software engineering and data engineering teams on system design, distributed computing, APIs, and production architecture
• Communicate technical trade-offs, system design decisions, model performance, and limitations clearly to technical and non-technical stakeholders
• Stay current with emerging ML and GenAI techniques, prototype new approaches, and assess their applicability to supply chain challenges
  
  

Minimum Qualifications

• Bachelor's degree in Computer Science, Software Engineering, Electrical Engineering or a related technical field
• 10+ years of industry experience in machine learning engineering, software engineering or applied machine learning
• Strong software engineering skills with experience building and deploying production systems
• Strong understanding of ML algorithms from an implementation and production perspective
  
  

Preferred Qualifications

• MS or PhD in Computer Science, Electrical Engineering, Statistics, Mathematics, or a related technical field
• Strong Python programming skills; experience with Java or C++ for production systems is a plus
• Experience with SQL and large-scale data processing
• Experience with modern ML frameworks such as PyTorch or TensorFlow
• Experience building and deploying transformer-based models and large language models
• Practical experience with LLM/GenAI applications, including agents, structured reasoning, RAG, or Text-to-SQL
• Experience designing and operating scalable model inference services and APIs
• Experience with MLOps, including model deployment, monitoring, evaluation, and production pipelines
• Experience with distributed computing and large-scale ML systems
• Experience optimizing ML models and systems for performance, latency, scalability, and efficiency
• Experience applying ML to forecasting, optimization, or operational decision-making problems
• Experience in Supply Chain, Operations, or related domains
• Strong system design skills and ability to work across ML, software, and data engineering
• Ability to operate independently and influence cross-functional stakeholders
  
  

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