Applied SciML (Scientific Machine Learning ) Engineer
Posted Updated
Role: Applied SciML (Scientific Machine Learning ) Engineer
Location: Santa Clara, CA
Location: Santa Clara, CA
MUST HAVE: Scientific Machine Learning, GPU, Pytorch, Computational
Geometry Experience
Job Description:
• 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
Job Description:
• 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
Requirements
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
• 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