UniversalAGI
AI + Computational Fluid Dynamics Researcher
UniversalAGI, San Francisco, California, United States, 94199
About UniversalAGI
UniversalAGI is an AI for Physics company based in San Francisco, backed by world-class investors including Elad Gil, Eric Schmidt, Ion Stoica, and David Patterson. We're building the foundational intelligence layer for physics - accelerating design, simulation, and experimentation across industries like aerospace, automotive, semiconductors, materials, and energy.
Our work integrates modern machine learning with computational physics, turning months-long engineering loops into real-time insight. We operate in person in San Francisco, with a small, high-velocity team of builders obsessed with customer outcomes, scientific depth, and shipping breakthroughs that matter.
If you thrive on technical intensity, learning from brilliant peers, and creating impact measured in equations solved and customers transformed - this is your place.
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.
Our work integrates modern machine learning with computational physics, turning months-long engineering loops into real-time insight. We operate in person in San Francisco, with a small, high-velocity team of builders obsessed with customer outcomes, scientific depth, and shipping breakthroughs that matter.
If you thrive on technical intensity, learning from brilliant peers, and creating impact measured in equations solved and customers transformed - this is your place.
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.