Cheap Jobs in Washington (NOW HIRING)

The Discovery team at Anduril is at the forefront of incubating and maturing high-potential, software-defined, AI-native offerings that meet the toughest, newest challenges across hardware, software, space, and cyber domains. We're the architects of mission autonomy and mesh networking, delivering scalable solutions that meet some of the most urgent national security needs. By working hand-in-hand with elite teams in Perception, AI, Motion Planning, Hardware, Test Engineering, Space, Networking, and Vehicle hardware, we craft cutting-edge, end-to-end systems that redefine mission success.

As a Red Team Engineer, you are the adversary to our own systems. Your job is to find the wrong assumption, the unmodeled failure mode, and the design weakness before it reaches the field - when it's still cheap to fix. This is not a slide-reviewing role: you bring hands-on experience designing, building, and analyzing real engineering systems, and you turn that builder's intuition into rigorous, quantitative adversarial analysis that changes decisions.

This is a critical, deeply cross-functional role. You will embed with and pressure-test the work of Systems Engineering, GNC, Software, Hardware, Mission Operations, and Test Engineering - independently reconstructing their analyses, breaking their models, and stress-testing their trade studies. Because our systems span domains - space, missiles, air, autonomy, sensors - you are expected to reason across all of them and follow a problem wherever it leads. The breadth and credibility of your analysis will be a critical element of mission success.

This role is directly tied to ongoing, funded programs within Anduril's Discovery Business Line, building and fielding resilient, software-defined systems across numerous mission threads on operationally relevant timelines

• Independently execute red-team designs, models, and analyses across the program - hunt failure modes, surface hidden assumptions, and pressure-test trade studies before decisions are locked in
• Contribute to the models and simulations needed to prove or break a claim, rather than taking it on faith - often developing the analysis tooling yourself
• Reconstruct and independently verify others' work; find the error everyone else missed
• Work shoulder-to-shoulder with Systems, GNC, Software, Hardware, Mission Ops, and Test teams, translating findings into clear, actionable guidance that measurably improves the design
• Bring hands-on systems-building experience to bear so problems are caught in analysis, not in flight
• Communicate complex results - including strong visual presentation of data - to engineers, leadership, and stakeholders

• Currently possesses and is able to maintain an active U.S. Top Secret security clearance
• Hands-on track record of both designing/building and analyzing real engineering systems - not analysis-only
• Demonstrated ability to find problems others miss: failure analysis, adversarial review, independent verification, or equivalent
• Proven track record of independently owning and executing deliverables with little to no management
• Proficiency with physics-math scripting (e.g., MATLAB, Simulink, Python + numpy/scipy)
• Strong engineering background in a relevant field (Aerospace, Dynamics & Controls, GNC, Systems, or similar)
• Outstanding communication skills, including visual presentation of complex data

• 4+ years in a relevant science/engineering field
• Hands-on design and analysis experience across multiple of: space systems, missiles / missile defense, air vehicles, GNC, sensors and sensor fusion, radar, EW. You are not expected to have all of these - the more breadth, the better
• Formal red-team, T&E, IV&V, mission-assurance, or failure-analysis experience
• Comfort moving fluidly between domains and physics regimes within a single problem
• Exceptional proficiency with physics-math scripting of your choice
• Familiarity with domain analysis tools as relevant (e.g., STK/Astrogator, AFSIM)
• Experience with genetic algorithms, machine learning, AI, and reinforcement learning