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Shield AI

Aerodynamics & Performance Engineer (R5732)

Dallas, Texas$115k–$175kfull time employeemidAdded today

About this role

Shield AI seeks an Aerospace Engineer in Dallas to serve as the technical authority on V-BAT aircraft performance, endurance, and mass properties. You'll develop validated aerodynamic and performance models to inform design decisions, payload integration, and autonomous flight software across design, propulsion, controls, and flight test teams.

What you'll do

  • Own flight performance, endurance, and mass properties analysis for V-BAT aircraft
  • Develop and validate aerodynamic models translating flight dynamics and aircraft configuration into performance predictions
  • Translate environmental conditions and design parameters into performance guidance for engineering teams
  • Support payload integration and autonomous flight software development with performance data and constraints
  • Collaborate with design, propulsion, guidance/controls, and flight test teams to improve aircraft capability
  • Conduct or support flight testing and correlate predictions with actual performance data

What they're looking for

  • Aerodynamics and aircraft performance modeling
  • Flight dynamics and control systems understanding
  • Mass properties and weight analysis
  • MATLAB, Python, or similar technical computing languages
  • Flight test data analysis and correlation
  • Propulsion system integration knowledge
  • Autonomous systems and guidance software familiarity
  • Communication across multidisciplinary engineering teams
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Shield AI

Shield AI develops autonomous defense aircraft platforms, including systems like the V-BAT, requiring sophisticated hardware integration and testing. The company is hiring mechanical engineers, electrical engineers, and supplier development specialists to design aircraft systems, develop test solutions, and manage aerospace supply chains.

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Likely interview questions

  • Describe your experience developing or validating aerodynamic models for aircraft design decisions.
  • How have you approached correlating predicted performance with actual flight test data?
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