Responsibilities
- Develop, train, evaluate, and deploy production-grade AI surrogate models that accelerate critical engineering simulation workflows
- Design and implement State-of-the-Art (SOTA) neural architectures and training strategies tailored to complex engineering problem domains
- Build scalable data pipelines to preprocess, manage, and utilize tens of thousands of high-fidelity simulation results
- Stay current with the latest research in neural operators, physics-informed ML, and surrogate modeling, implementing new techniques when needed
- Collaborate with peers on architecture, design, and code reviews
- Deep dive into engineering problems to identify where AI can deliver the highest leverage and most reliable solutions
- Develop and apply techniques for uncertainty quantification, active learning, and inverse problems (e.g., geometry and shape optimization)
- Ensure all AI systems are rigorously validated and vetted for accuracy, robustness, and reliability in engineering use
Basic qualifications
- Bachelor's degree in computer science, data science, engineering, math, physics, or a related technical discipline; OR 4+ years of professional experience building software in lieu of a degree
- 1+ years of software development experience in Python for machine learning, AI, or data science applications
- Master's or PhD in computer science, machine learning, engineering, or a related field with a focus on surrogate modeling or AI for scientific/engineering simulation
- Demonstrated experience training, tuning, and deploying production-grade ML surrogate models in real engineering workflows
- Expert-level understanding of at least one modern architecture class such as Fourier Neural Operators (FNO), neural operators, MeshGraphNet, Transolver, graph neural networks, physics-informed neural networks, or other surrogate model architecture
- Experience solving inverse problems such as geometry optimization or design under uncertainty
- Strong understanding of traditional simulation and numerical methods (CFD, FEA, thermal analysis, etc) and how to integrate them with surrogate models
- Experience with uncertainty quantification techniques for surrogate models
- Hands-on experience building active learning or adaptive sampling pipelines
- Proficiency with deep learning frameworks such as PyTorch, TensorFlow, or JAX
- Experience with surrogate modeling libraries such as NVIDIA PhysicsNemo or similar
- Experience developing on Linux systems with GPU accelerators
- Strong understanding of software engineering best practices including version control, testing, and continuous integration
- Solid foundation in statistics, numerical methods, and core machine learning algorithms
- Ability to work extended hours and weekends as necessary
Benefits
- To conform to U.S. Government export regulations, applicant must be a (i) U.S. citizen or national, (ii) U.S. lawful, permanent resident (aka green card holder), (iii) Refugee under 8 U.S.C. § 1157, or (iv) Asylee under 8 U.S.C. § 1158, or be eligible to obtain the required authorizations from the U.S. Department of State. Learn more about the ITAR here.
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