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Salary: USD 166,000 - 0 / annual
Anduril Industries is seeking a Red Team Engineer for the Discovery business line to serve as an independent adversary to the company's defense systems. This role focuses on identifying design weaknesses, unmodeled failure modes, and hidden assumptions before systems reach the field—when fixes are still cost-effective.
You will embed with cross-functional teams spanning Systems Engineering, Guidance Navigation & Control (GNC), Software, Hardware, Mission Operations, and Test Engineering. Your responsibilities include independently executing red-team analyses across programs, hunting failure modes, pressure-testing trade studies, and reconstructing others' work to find overlooked errors. You'll develop models and simulations to prove or break claims, contribute analysis tooling, and translate findings into actionable guidance that measurably improves designs.
This is a hands-on role requiring deep technical expertise. You will reason across multiple domains—space, missiles, air vehicles, autonomy, sensors—and follow problems wherever they lead. Strong communication skills are essential, including visual presentation of complex data to engineers, leadership, and stakeholders.
Required qualifications include an active U.S. Top Secret security clearance, hands-on experience both designing/building and analyzing real engineering systems, demonstrated ability to find problems others miss (failure analysis, adversarial review, independent verification), proven track record of independently owning deliverables, proficiency with physics-math scripting (MATLAB, Simulink, Python), and a strong engineering background in aerospace, dynamics & controls, GNC, systems, or similar fields.
Preferred qualifications include 4+ years in relevant science/engineering, hands-on experience across multiple domains (space systems, missiles, air vehicles, GNC, sensors, radar, EW), formal red-team or test & evaluation experience, comfort moving between domains and physics regimes, exceptional scripting proficiency, familiarity with domain analysis tools (STK/Astrogator, AFSIM), and experience with genetic algorithms, machine learning, AI, and reinforcement learning.
