Scientific Machine Learning Engineer — Uncertainty Quantification Dridam Flight | Remote — United States | Full-time About the roleDridam Flight is hiring a hands-on Scientific Machine Learning Engineer to develop trustworthy predictive models and uncertainty quantification (UQ) capabilities for aerospace digital twins. You will work with physics-based ML, nonlinear dynamical systems, and simulation data to improve how model predictions are evaluated and used. We welcome engineers from aerospace, robotics, autonomous systems, scientific computing, and other research-intensive fields. The role is approximately 80% implementation and 20% applied research. What you will do• Implement and validate UQ and uncertainty propagation for scientific ML models and digital twins. Build and test surrogate models of nonlinear dynamical systems using simulation data. Assess model calibration, prediction-interval coverage, and limitations against held-out data. Quantify influential inputs and perform sensitivity analyses. Deliver readable, tested scientific Python code and communicate results to the engineering team. What we are looking for• Hands-on experience implementing UQ or probabilistic models for numerical or ML systems. Ability to explain uncertainty sources, propagation, calibration, and validation. Strong scientific Python and numerical computing skills. Experience modeling physical systems or working with physics-based simulation data. Ability to select and implement appropriate methods independently. Preferred experience• Experience with physical modeling or simulation in aerospace, robotics, autonomous vehicles, energy systems, computational physics, or other technical domains. Experience developing reduced-order, surrogate, or physics-informed predictive models for complex dynamical systems. Experience with uncertainty quantification, probabilistic inference, model calibration, or data assimilation. PyTorch, JAX, NumPy, SciPy, or C++; an advanced degree, one specific algorithm, or prior aerospace employment is not required. Work arrangement and hiring processFull-time W-2; remote within the United States, with occasional travel. U.S. citizenship is required for this position. An active U.S. security clearance is preferred but not required. The selection process includes an initial recruiter conversation, a possible project discussion, and one coordinated interview day of four to five short one-on-one conversations. No take-home assignment. Compensation and benefits$135k–$190k plus benefits.