ML Engineer, Surrogate Modeling (Vehicle Engineering)

ML ENGINEER SURROGATE MODELING (VEHICLE ENGINEERING)

Be a member of the AI for Vehicle Engineering team focusing on developing high-performance surrogate models to solve complex physics and engineering problems for our launch vehicles and spacecraft.

Our team builds AI systems that accelerate engineering analysis simulation development testing avionics design flight data review logistics and mission operations. Your work will directly support the worlds largest communication and AI satellite constellations accelerate rapid reuse of the Falcon launch vehicle and contribute to the development of the worlds largest rocket capable of sending humans to Mars.

In this role you will develop train tune and deploy AI surrogate models to dramatically accelerate engineering simulations including but not limited to FEA CFD thermal and structural analysis. You will work closely with hardware simulation and domain engineers to build these systems from the ground up. You will leverage state-of-the-art surrogate modeling techniques and create novel methodologies that push the frontier of what is possible in ML for physics while tackling real-world problems.

Aerospace experience is not required. We are looking for smart motivated collaborative engineers who love applying machine learning to hard scientific problems and want to make a direct impact on SpaceXs mission.

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:

• Bachelors 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

PREFERRED SKILLS:

• Masters 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

ADDITIONAL REQUIREMENTS:

• Ability to work extended hours and weekends as necessary

COMPENSATION AND BENEFITS:
Pay Range:
AI Software Engineer/Level I: $125000.00 - $145000.00/per year
AI Software Engineer/Level II: $145000.00 - $175000.00/per year

Your actual level and base salary will be determined on a case-by-case basis and may vary based on the following considerations: job-related knowledge and skills education and experience.

Base salary is just one part of your total rewards package at SpaceX. You may also be eligible for long-term incentives in the form of company stock stock options or long-term cash awards as well as potential discretionary bonuses and the ability to purchase additional stock at a discount through an Employee Stock Purchase Plan. You will also receive access to comprehensive medical vision and dental coverage access to a 401(k) retirement plan short and long-term disability insurance life insurance paid parental leave and various other discounts and perks. You may also accrue 3 weeks of paid vacation and will be eligible for 10 or more paid holidays per year. Employees accrue paid sick leave pursuant to Company policy which satisfies or exceeds the accrual carryover and use requirements of the law.