Senior ML Engineer, Core Development

What you'll do

• Design, train, and deploy production-grade surrogate models to accelerate CFD, FEA, thermal, and structural simulation workflows.
• Develop neural architectures tailored to engineering physics, including techniques for uncertainty quantification and inverse problems.
• Build data pipelines to extract, aggregate, and sanitize high-fidelity results from various solver outputs.
• Optimize model inference for the design loop to maximize GPU utilization and achieve interactive-speed latency.
• Integrate surrogate model predictions into existing engineering tools used by domain specialists.
• Identify high-leverage applications for machine learning across the air vehicle design portfolio.
• Provide technical mentorship to non-ML engineers on physics-informed AI applications.

What we're looking for

• Must be a U.S. Person eligible to obtain and maintain a U.S. Top Secret security clearance.
• BS, MS, or PhD in aerospace, thermal, mechanical, or electrical engineering, or in machine learning/AI/data science with a demonstrated engineering foundation.
• 3+ years of experience taking ML models from R&D into production using large-scale scientific or engineering datasets.
• Working knowledge of modern surrogate architectures (e.g. GNNs, Transolver, DoMINO & GeoTransolver) and experience running physical simulations (CFD, FEA, thermal, etc.).
• Proficiency in Python and MATLAB, with experience in PyTorch, TensorFlow, and NVIDIA PhysicsNeMo (Modulus).
• Experience developing on Linux with GPU accelerators and distributed training.
• Track record of building production data pipelines from heterogeneous engineering sources, utilizing uncertainty quantification, and conducting statistical analysis.
• Graduate research focused on AI for scientific simulation and experience solving inverse problems (preferred).