ML Engineer, Surrogate Modeling (Vehicle Engineering)
Core
Develop high-performance AI surrogate models to accelerate complex physics and engineering simulations for launch vehicles and spacecraft.
Role type
Senior IC machine-learning engineer (surrogate modeling)
Builds
AI systems accelerating engineering analysis, simulation, development, testing, avionics design, flight data review, logistics, and mission operations
Domain
Aerospace engineering / Physics-informed machine learning
Deliverable
production ML models
Required skills
Python for machine learning, neural architecture design, scalable data pipeline construction, uncertainty quantification, active learning, inverse problem solving, software engineering best practices
Preferred skills
Expert knowledge of neural operators (FNO, MeshGraphNet, Transolver), physics-informed neural networks, traditional simulation methods (CFD, FEA), PyTorch/TensorFlow/JAX, NVIDIA PhysicsNemo, Linux/GPU development
Technologies
PyTorch, TensorFlow, JAX, NVIDIA PhysicsNemo, Linux, GPU accelerators
Responsibilities
Develop, train, evaluate, and deploy production-grade AI surrogate models; Design and implement SOTA neural architectures; Build scalable data pipelines for high-fidelity simulation results; Stay current with research in neural operators and physics-informed ML; Collaborate on architecture and code reviews; Identify high-leverage AI opportunities in engineering problems; Apply techniques for uncertainty quantification and active learning; Validate AI systems for accuracy and robustness
Seniority
Senior, hands-on IC