Staff Engineer, System Reliability (R5533)

Shield AI is looking for a Reliability Modeling Engineer to own the reliability model and failure-prediction strategy for the V-BAT unmanned aircraft system. In this role you will build the quantitative backbone that predicts how the system fails, why it fails, and how to design those failures out — translating field data, industry sources, and test results into models that directly inform design, production, and sustainment decisions. You will be the reliability subject-matter expert who partners across Design, Manufacturing, Quality, and Fleet Support to drive measurable improvements in system reliability.

Build and maintain the V-BAT system reliability model, predicting system-level failure rate using failure-rate data from the field and industry sources.

Create and maintain the V-BAT system fault tree, identifying single points of failure and quantifying their contribution to overall system reliability.

Inform design decisions that eliminate single points of failure and improve the reliability of the system, providing Design-for-Reliability (DfR) guidance in design reviews.

Partner with Design, Fleet Support, Quality, and Manufacturing Engineers on root cause investigations for field failures, and drive corrective and preventive actions to closure.

Drive reliability test campaigns in production and sustainment to verify the system is meeting its reliability requirements and predictions, feeding results back into the model.

Drive production reliability screening campaigns to reduce infant-mortality failures, such as HASS (Highly Accelerated Stress Screening) and ESS (Environmental Stress Screening).

Establish and track reliability metrics — MTBF, failure-rate trends, and reliability growth — and communicate reliability status and risk posture to program leadership.

B.S. in Engineering, Mathematics, or Statistics discipline.

Typically requires a minimum of 7 years of related experience in reliability or surety engineering in a high-consequence industry (defense, aerospace, automotive, or medical device).

Expertise in Fault Tree Analysis (FTA), Failure Modes, Effects, and Criticality Analysis (FMECA), and Mean Time Between Failure (MTBF) calculations.

Demonstrated ability to build reliability models from field and test data and translate them into actionable design and production decisions.

Experience with Reliability, Availability, and Maintainability (RAM) modeling and Weibull analysis.

Experience with reliability analysis software such as Relyence (or equivalent tools such as ReliaSoft/Weibull++, Windchill Risk & Reliability).

Familiarity with government standards for developing reliability and maintainability models, such as MIL-HDBK-217, 217Plus, and MIL-STD-756.

Experience implementing FRACAS (Failure Reporting, Analysis, and Corrective Action System) and supporting Failure Review Boards.

CRE (ASQ Certified Reliability Engineer) certification.

Experience with unmanned aircraft, aerospace, or other mission-critical hardware systems.

Full-time regular employee offer package:

Pay within range listed + Bonus + Benefits + Equity

 

Temporary employee offer package:

Pay within range listed above + temporary benefits package (applicable after 60 days of employment)

 

Salary compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, licenses and certifications, and specific work location. All offers are contingent on a cleared background and possible reference check. Military fellows and part-time employees are not eligible for benefits. Please speak to your talent acquisition representative for more information.

 

###

 

Shield AI is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender identity or Veteran status. If you have a disability or special need that requires accommodation, please let us know.