ML Engineer, Surrogate Modeling (Vehicle Engineering)
Actively Hiring
Full-time $125k - $175k Posted 2 months ago
Responsibilities
- check_circle Develop, train, evaluate, and deploy production-grade AI surrogate models that accelerate critical engineering simulation workflows
- check_circle Design and implement State-of-the-Art (SOTA) neural architectures and training strategies tailored to complex engineering problem domains
- check_circle Build scalable data pipelines to preprocess, manage, and utilize tens of thousands of high-fidelity simulation results
- check_circle Stay current with the latest research in neural operators, physics-informed ML, and surrogate modeling, implementing new techniques when needed
- check_circle Collaborate with peers on architecture, design, and code reviews
- check_circle Deep dive into engineering problems to identify where AI can deliver the highest leverage and most reliable solutions
- check_circle Develop and apply techniques for uncertainty quantification, active learning, and inverse problems (e.g., geometry and shape optimization)
- check_circle 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
- check_circle 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.
Tags & Focus Areas
Ai Machine Learning
About SpaceX
SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today SpaceX is actively developing the technologies to make this possible, with the goal of enabling human life on Mars. SR. CYBER ASSURANCE ANALYST Assurance is more than doing what is forced upon us; it's about driving and delivering against our trust proposition, enabling teams across the company to meet the standards we set upon...