What You’ll Do
• Build hybrid models that merge physics-based solvers with machine learning.
• Experiment with neural operators, PINNs, and surrogate modeling for high-fidelity CFD data.
• Collaborate with engineers to test and validate models on real-world geometries.
• Productionize research that advances the frontier of physics-informed AI.
Who You Are
• PhD or strong research background in Mechanical/Aerospace Engineering, Applied Physics, or Machine Learning.
• Expertise in scientific computing, numerical methods, and model development.
• Experience with TensorFlow/PyTorch and simulation frameworks.
• Hands-on, iterative, and results-oriented researcher.
Nice to Have
• Experience in multi-scale modeling or uncertainty quantification.
• Prior industry collaboration or customer-facing applied research.
All roles are full-time and based in person at our San Francisco office.
Top Skills
What We Do
UniversalAGI is automating physical systems engineering across the entire product lifecycle with artificial intelligence.
