ML Engineer, Surrogate Modeling (Vehicle Engineering)
Tasks
- Apply uncertainty quantification active learning and inverse problem methods for optimization
- Build scalable data pipelines for high fidelity simulation results
- Collaborate on architecture, design, and code reviews
- Design and implement neural architectures and training strategies for complex engineering domains
- Develop train evaluate and deploy production grade AI surrogate models for engineering simulation workflows
- Identify engineering problems where AI provides high leverage and reliable solutions
- Implement surrogate modeling techniques including neural operators physics informed ML and uncertainty quantification
- Validate accuracy robustness and reliability of AI systems for engineering use
Perks/Benefits
- 401k retirement plan
- Employee stock purchase plan
- Life insurance
- Long-term disability insurance
- Long-term incentives
- Medical, dental & vision coverage
- Paid Holidays
- Paid parental leave
- Paid sick leave
- Paid vacation
- Short-term Disability Insurance
- Stock options
Skills/Tech-stack
Active Learning | Adaptive Sampling | CFD | Continuous integration | Data Pipelines | Deep learning | FEA | Fourier Neural Operator | GPU | Graph Neural Networks | Inverse problems | JAX | Linux | Machine Learning | Neural Networks | Neural Operator | Neural operators | Physics Informed Neural Networks | PyTorch | Python | Structural analysis | TensorFlow | Testing | Thermal analysis | Uncertainty Quantification | Uncertainty Quantification Techniques | Version control
Education
Roles
AI | AI Engineer | Engineer | ML Engineer
Regions
Countries
States
Cities
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