AI Assurance Engineer

Johns Hopkins Applied Physics Laboratory (APL) seeks an AI Assurance Engineer to ensure mission-critical AI systems are safe, robust, and trustworthy. You will design assurance frameworks, tests, and metrics to evaluate model reliability, fairness, security, and performance for national security, space, and health applications. Partnering with leading researchers and engineers, you'll embed assurance into the full AI lifecycle, from concept through deployment, and translate ethical and policy requirements into technical controls in APL's collaborative, mission-driven environment.

Responsibilities

• Design and implement AI assurance and validation frameworks for mission-critical systems.
• Develop tests and metrics to evaluate robustness, fairness, security, and reliability of AI models.
• Collaborate with researchers, engineers, and domain experts to integrate assurance into AI system design.
• Conduct model risk assessments and document assurance evidence for stakeholders and sponsors.
• Build tools and pipelines for continuous monitoring and regression testing of deployed AI.
• Translate ethical, legal, and policy requirements into technical assurance criteria.
• Analyze system failures, edge cases, and adversarial behaviors to improve AI safety.
• Support research on next-generation trustworthy, explainable, and robust AI methods.

Required Skills

• AI/ML model development and evaluation
• Python programming
• Machine learning frameworks (e.g., Tensor
• Flow, Py
• Torch)
• Software testing and QA automation
• Model risk assessment and validation
• Data analysis and visualization
• MLOps and CI/CD for ML systems
• Security and adversarial robustness techniques
• Fairness, bias, and explainability methods
• Requirements engineering and technical documentation