Operations Reliability Engineer

Apple•Cupertino, CA

About The Position

At Apple, Operations Reliability Engineers guide development and operations teams toward reliable designs for new technology components, modules, and products. They identify high-risk failure modes early in the design life cycle and use engineering knowledge to help determine effective mitigations before ramp. The role collaborates with Apple module and system reliability engineering teams to lead operational readiness activities and support a smooth transition from validated prototypes to full-scale production. The engineer facilitates problem resolution by focusing cross-functional teams on reliability issues and partnering with Failure Analysis engineers to identify root causes and validate corrective actions. During product ramp and full production, the role serves as a gatekeeper of reliability risk and makes data-driven recommendations for addressing reliability excursions and newly identified failure modes. The engineer leads Ongoing Reliability Testing (ORT), accounting for material, process, design robustness, supply chain, and final assembly effects. The role partners with sustaining and AppleCare teams to improve the reliability experience through better factory detection and improved ORT, and works with reliability and design teams to apply lessons learned to future designs. It also involves inventing more efficient testing methods with higher detection rates and investigating how big data and machine learning can enhance reliability testing and create early warning systems. Internal travel of up to 20% is required.

Requirements

At least 5 years of experience performing reliability testing, including mechanical stress tests such as shock, drop, and vibration testing, as well as environmental and fatigue testing.
Experience with Failure Analysis analytical and material characterization techniques, including Optical Microscopy, X-Ray, SEM/EDS, FTIR, and XPS.
Bachelor of Science in Engineering in Mechanical, Electrical, Reliability, Materials, Optical, Industrial, or a similar discipline.
Strong understanding of the physics behind mechanical, electrical, and optical material properties.
Ability to use fundamental failure analysis methodology to derive failure root causes.
Knowledge of reliability models, accelerated test models, and industry test standards such as JEDEC, MIL, ASTM, and IEEE.
Deep understanding of the application of DOE (Design of Experiments) and FMEA (Failure Modes and Effects Analysis).
Strong statistical and data analytics skills, including statistical process control and sampling methods.
Problem-solving ability, attention to detail, and ability to handle multiple projects simultaneously.
Dynamic, can-do attitude and desire to work with a team and product.
Clear and consistent communication, including executive-level presentation skills, and ability to drive cross-functional teams.

Nice To Haves

Master's or Ph.D. in Engineering in Mechanical, Electrical, Reliability, Materials, Optical, Industrial, or a similar discipline.

Responsibilities

Guide development and operations teams toward reliable designs for new technology components, modules, and products.
Identify high-risk failure modes early in the design life cycle and help determine mitigations before ramp.
Lead operational readiness activities with module and system reliability engineering teams to support transition from validated prototypes to full-scale production.
Drive cross-functional problem resolution for identified reliability issues and partner with Failure Analysis engineers to establish root causes and validate corrective actions.
Manage reliability risk during product ramp and full production, making data-driven recommendations about reliability excursions and newly identified failure modes.
Lead Ongoing Reliability Testing (ORT), accounting for material, process, design robustness, supply chain, and final assembly effects.
Partner with sustaining and AppleCare teams to improve reliability through better factory detection and improved ORT.
Collaborate with reliability and design teams to apply lessons learned to future designs.
Develop new testing approaches to improve efficiency and increase detection rates.
Investigate using big data and machine learning to enhance reliability testing and create early warning systems.
Travel internally up to 20%.