Applied SciML Engineer

Applied SciML Engineer We're looking for an engineer with deep expertise in scientific machine learning and computational geometry to build production ML systems for physical simulation data. Requirements Core Deep Learning Experience (3+ years) ● Hands-on deep learning training with scientific datasets: CAD geometries, CFD simulations, or similar physical data ● End-to-end model development: data preparation, training, hyperparameter tuning ● Multi-GPU training and GPU memory optimization ● PyTorch and PyTorch Lightning proficiency Technical Depth Demonstrated experience (via publications in top venues OR 3+ years industry experience) in one or both areas: ● Computational Geometry: PointNet, DGCNN, TripNet, MeshGPT, or similar geometric deep learning architectures ● Scientific ML: PINNs, DeepONet, FNO, Transformers for physics, SCoT, Poseidon, or related physics-informed models Evidence of Impact Publications in leading ML/computational science conferences/journals OR proven track record building SciML systems in industry.

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