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
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
Minimum Qualifications
Bachelor’s degree in Computer Science, Software Engineering, Electrical Engineering or a related technical field
4+ 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
