During my 7-month Manufacturing Quality Engineering (MQE) co-op at Apple, I worked at the intersection of design, manufacturing, supply chain, and data to help ensure consumer hardware met aggressive quality, reliability, and schedule requirements at scale.
Because the majority of deliverables were proprietary, this case study focuses on the engineering problems, workflows, and decision-making frameworks I developed rather than specific product artifacts.
Translating design intent into manufacturable, testable, and measurable requirements
Analyzing production line yield, reliability testing, and field-relevant data to identify trends and failure modes
Supporting manufacturing readiness and ramp activities, where timelines compressed and data quality evolved rapidly
Collaborating across design engineering, operations, and suppliers to converge on production-viable solutions
These problems required balancing engineering rigor with real-world manufacturing constraints, often with incomplete or noisy data.
I focused on building repeatable, decision-oriented workflows that could scale with production volume and uncertainty.
Typical workflow:
Frame ambiguous manufacturing issues into a clear technical problem statement
Identify leading indicators and key quality metrics
Perform targeted data analysis to isolate root causes or risk drivers
Propose corrective actions or trade-offs grounded in manufacturability and reliability
Communicate findings through concise technical reviews to cross-functional stakeholders
This approach emphasized clarity, speed, and traceability, especially during later builds when rapid iteration was critical.
Statistical analysis of production and reliability data
Failure analysis and risk assessment
GD&T interpretation and tolerance reasoning
Supplier and contract manufacturer interaction
Cross-functional technical communication
Python, JMP, and internal data analysis tools
This role reinforced how robust engineering decisions emerge under constraint—limited time, imperfect data, and real production consequences. It strengthened my ability to:
Operate effectively in high-stakes, high-confidentiality environments
Make engineering trade-offs grounded in both physics and manufacturability
Communicate technical conclusions clearly to diverse stakeholders