Physical AI Engineer - SW

📋 Description Build/train/validate ML models that approximate physics with uncertainty. Generate synthetic datasets for training and stress-testing models. Integrate models with CFD/FEA and optimization tools for fast answers. Integrate foundation models into engineering workflows with human oversight. Create reliable, traceable ML outputs for engineers to trust. Ship research to production in a typed Python monorepo with reproducibility. 🎯 Requirements Strong Python skills and ML pipelines in real, version-controlled codebases. Hands-on ML: train, evaluate, and debug models for production-ready implementations. Knowledge of physics-informed ML, neural operators, or surrogate modeling. Experience generating or using synthetic data to train systems. Judgment on foundation models; know when to trust or verify. Evidence-first mindset; explicit data/verification for outputs. BSc/MSc or equivalent in quantitative/engineering field. Solid Git and modern software practices. Excellent cross-disciplinary communication and collaboration. Genuine aviation interest and learning systems for real-world use. 🎁 Benefits Aerospace background; CFD/FEA or MDAO familiarity. Differentiable optimization or enforcing physical constraints in learned models. Exposure to safety-critical or regulated environments. Sim-to-real techniques and reconciling with hardware/flight data. Lab instrumentation experience (oscilloscopes, analyzers, HIL/SIL rigs). Fluency with scientific Python and ML stacks (PyTorch/JAX, async services, queues, time-series DBs). Understanding model-scaling principles and trade-offs.