Applied SciML (Scientific Machine Learning ) Engineer Job at Govserviceshub in S
Govserviceshub, Santa Clara, CA, United States
Applied SciML (Scientific Machine Learning) Engineer
Santa Clara, United States | Posted on 10/16/2025
Role: Applied SciML (Scientific Machine Learning) Engineer
Location: Santa Clara, CA
MUST HAVE:
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, hyper‑parameter 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 a proven track record building SciML systems in industry
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Santa Clara, United States | Posted on 10/16/2025
Role: Applied SciML (Scientific Machine Learning) Engineer
Location: Santa Clara, CA
MUST HAVE:
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, hyper‑parameter 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 a proven track record building SciML systems in industry
#J-18808-Ljbffr