ML Engineer, Surrogate Modeling (Vehicle Engineering)
Tasks
- Build scalable data pipelines for simulation results
- Collaborate on architecture, design, and code reviews
- Design neural architectures and training strategies
- Develop train evaluate and deploy production grade AI surrogate models for engineering simulations
- Develop uncertainty quantification and active learning methods
- Implement neural operators physics informed ML and surrogate modeling techniques
- Integrate surrogate models with simulation and numerical methods
- Validate accuracy robustness and reliability for engineering use
Perks/Benefits
- 401k retirement plan
- Dental insurance
- Employee stock purchase plan
- Long-term incentives
- Medical insurance
- Paid Holidays
- Paid parental leave
- Paid sick leave
- Paid vacation
- Vision insurance
Skills/Tech-stack
Active Learning | Adaptive Sampling | CFD | Continuous integration | Deep learning | FEA | Fourier Neural Operators | GPU Computing | Graph Neural Networks | Inverse problems | JAX | Linux | Machine Learning | MeshGraphNet | Neural Networks | Neural operators | Numerical Methods | Physics Informed Neural Networks | PyTorch | Python | Structural analysis | TensorFlow | Testing | Thermal analysis | Uncertainty Quantification | Version control
Education
Regions
Countries
States
Cities
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