Artificial Intelligence Subject Matter Expert

About Agile Defense   At Agile Defense we know that action defines the outcome and new challenges require new solutions. That’s why we always look to the future and embrace change with an unmovable spirit and the courage to build for what comes next.   Our vision is to bring adaptive innovation to support our nation's most important missions through the seamless integration of advanced technologies, elite minds, and unparalleled agility—leveraging a foundation of speed, flexibility, and ingenuity to strengthen and protect our nation’s vital interests.
Title: Artificial Intelligence Subject Matter Expert (AI SME) Clearance: Active Top Secret with SCI eligibility, ability to obtain and maintain a CBP Background Investigation (CBP BI) and EOD, active BI strongly preferred. We can begin processing for candidates who do not hold one. Citizenship: U.S. Citizenship required Location: Ashburn, VA Salary Range: $180,000-235,000 Signing Bonus: $10,000 for candidates with an active CBP BI. Payable after 90 days; standard terms apply.  

The Role

U.S. Customs and Border Protection runs continuous operations across more than 300 land, air, and sea ports of entry, plus Border Patrol stations and the Air and Marine Operations Center. Applying artificial intelligence in that environment is not the same problem as applying it somewhere the cost of a wrong answer is a bad recommendation. A model that runs against operational data here has to be accurate, explainable, and safe under federal accreditation, or it does not ship, no matter how well it performs in a lab.   You are the technical authority on what that requires. You will design, test, and align AI and machine learning models so they perform reliably in a professional operational setting, working alongside program leadership, engineering teams, and the security staff who have to sign off on what you build before it runs against real data.   Two things are worth knowing before you apply. This role exists to bring rigor, not to chase what is newest. And a meaningful part of the job is being the person in the room who can tell the difference between a model that looks impressive in a demo and one that is actually safe to run.

What Success Looks Like

Objective 1: Design models that are accurate enough to trust with real decisions

• Model performance gets validated against real operational conditions, not only against a clean benchmark dataset.
• Where a model is wrong, you can say how it is wrong and how often, rather than reporting a single accuracy number and stopping there.
• You can explain a model's behavior to someone who did not build it, in terms they can evaluate.

Objective 2: Keep models safe and aligned once they are running against real data