Senior ML Engineer, Core Development
Core
Develop, train, and deploy production-grade surrogate models to accelerate physics simulations (CFD, FEA, thermal, structural) for autonomous air vehicle design.
Role type
Senior IC machine learning engineer (physics-informed surrogate modeling)
Builds
Surrogate modeling stack, training infrastructure, and simulation data pipelines for air vehicle programs
Domain
Defense / Aerospace engineering / Physics-informed machine learning
Deliverable
production ML models
Required skills
Physics-informed ML (surrogate modeling, GNNs, Transolver, DoMINO, GeoTransolver), Python, MATLAB, PyTorch, TensorFlow, NVIDIA PhysicsNeMo, Linux, GPU distributed training, uncertainty quantification, active learning, inverse problems, CFD/FEA/thermal simulation knowledge, data pipeline engineering
Preferred skills
Graduate research in AI for scientific simulation, geometry optimization, adaptive sampling pipelines, aerospace/turbomachinery domain expertise, commercial solver familiarity, ML Ops orchestration (Docker, W&B, AWS SageMaker), visualization tools (Plotly, Seaborn, Matplotlib)
Technologies
PyTorch, TensorFlow, NVIDIA PhysicsNeMo, Python, MATLAB, Linux, Docker, AWS S3, Lambda, SageMaker, W&B, Plotly, Seaborn, Matplotlib
Responsibilities
Design and implement neural architectures tailored to engineering physics; create pipelines for extracting and sanitizing high-fidelity solver outputs; optimize inference for the design loop; partner with domain engineers to identify high-leverage ML applications; provide technical mentorship to non-ML engineers
Seniority
Senior, hands-on IC