Staff Engineer, System Reliability (R5533)
Shield AI · On-site
MeritLog read this listing from Shield AI's Lever job board and last checked it on September 10, 2026.
Source: the employer's Lever job board. Open the original listing for current details.
Job details
- Work model
- On-site
- Salary
- $140,000 - $210,000
- Location
- Dallas, Texas
Hiring context
How this role compares at Shield AI
Shield AI has 484 live roles in MeritLog’s catalog across 12 job families, and 378 of them are in engineering. 376 of those listings publish a pay range, a disclosure rate of 78%.
This role's posted range of $140,000 - $210,000 sits above 36% of the 356 other Shield AI roles quoted over the same currency and period.
Shield AI concentrates this hiring in:
Counted across the job boards MeritLog tracks, at the time this page was served. Pay comparisons use only listings that publish a complete range in the same currency and period.
Job description
Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube. Job Description: 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.
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