Expert Reliability Engineer

Overview

We are seeking a highly analytical Expert Reliability Engineer to lead reliability efforts within our commercial desktop and notebook engineering teams. You will be the lead technical authority for reliability on key product programs, ensuring the rigorous application of Design for Reliability (DFR) methodologies from early R&D through high-volume manufacturing. You will apply life modeling, advanced statistical analysis, and environmental stress testing to ensure our products meet the high uptime and durability standards of our global customers.

Working cross-functionally, you will partner with engineering, procurement, and Original Design Manufacturer (ODM) teams to identify failure modes, quantify design margins, and drive data-driven resolutions to complex technical challenges.

• Reliability Execution & Program Leadership: Implement reliability goals and validation strategies for specific notebook and desktop programs. Apply internal reliability benchmarks and DFR principles to ensure market-segment requirements are met.

• Accelerated & Environmental Stress Testing: Design and execute accelerated stress testing protocols, including thermal/stress testing,
  HALT (Highly Accelerated Life Testing), and HASS (Highly Accelerated Stress Screening). Validate supplier and vendor reliability claims through rigorous data analysis.

• Root Cause & Failure Analysis: Lead technical failure analysis and root cause investigations for complex electrical and mechanical issues (e.g., PCBA fatigue, thermal throttling, high-speed signal integrity). Recommend and verify corrective actions for engineering and project leadership.

• Reliability Data Analysis: Utilize numerical and statistical analysis methods to assess test and field data, employing Weibull, Lognormal, and Exponential distributions to predict failure rates and inform warranty risk management.

• Cross-Functional Collaboration: Serve as the reliability lead on program core teams, collaborating with ODMs and internal hardware teams to negotiate design options and ensure hardware adheres to strict reliability standards.

• Design Margin Quantification: Conduct assessments to understand and quantify design margins, ensuring products are robust against varied environmental stressors and usage profiles.

The successful candidate must demonstrate a high level of proficiency in:

• Life Modeling & Physics of Failure: Proficiency in life modeling using Arrhenius (thermal stress), Coffin-Manson (thermal fatigue), and other industry-standard models to predict product longevity.

• Design of Experiments (DOE): Strong proficiency in statistical design of experiments, specifically Taguchi methods, to optimize design robustness and minimize variance.

• Statistical Validation: Ability to conduct Confidence Interval testing and Regression Analysis to validate reliability claims and performance data.

• PC Stressors: Deep familiarity with PC-specific environmental stressors and test procedures, including mechanical shock, temperature, vibration, humidity, and electromagnetic interference (EMI).

• Education: Bachelor’s or Master’s degree in Reliability Engineering, Materials Engineering, Electrical Engineering, Mechanical Engineering, or a related STEM field.

• Experience: Typically 6–10 years of experience in reliability modeling, hardware validation, or product engineering.

• Proven track record: Delivering high-volume consumer or commercial electronics.

• Preferred
  
  Certifications:
  
  ASQ Certified Reliability Engineer (CRE) is strongly preferred.

• Analytical Tools: Proficiency in reliability data analysis software (e.g., Relia Soft, JMP, Minitab) and familiarity with electrical design/simulation tools.

• Hardware Knowledge: Strong understanding of integrated circuitry, power management, thermal management, and mechanical wear items in mobile and desktop form factors.

• Project Leadership: Ability to lead technical projects and cross-functional work streams to improve reliability and manufacturing processes.

• Communication: Ability to translate complex failure modes and statistical analysis results into actionable…