Software Engineer, Engineering Simulation & Automation (Vehicle Engineering)

SpaceX • Hawthorne, CA

5d

About The Position

Be a member of the AI for Vehicle Engineering team, focusing on developing automated simulation tools and pipelines to accelerate engineering for our launch vehicles and spacecraft and generate high-quality training datasets for surrogate models at scale. 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 world’s largest communication and AI satellite constellations, accelerate rapid reuse of the Falcon launch vehicle, and contribute to the development of the world’s largest rocket capable of sending humans to Mars. In this role, you will design, develop, and maintain scalable simulation automation pipelines that create the large, diverse, and well-curated datasets required to train production-grade AI surrogate models. You will also work closely with domain experts to unblock engineering and analysis bottlenecks at SpaceX via simulation automation. You will leverage commercial, in-house, and open source simulation tools across FEA, CFD, thermal, and structural domains, writing custom scripts against their APIs to automate geometry parameterization, meshing, solving, post-processing, and data extraction at scale. You will also create general automation tools to accelerate day-to-day engineering workflows. You will work closely with ML surrogate modeling engineers, domain experts, and hardware engineers to ensure generated data and tools optimally support AI surrogates and broader engineering productivity.

Requirements

• Bachelor’s degree in engineering, computer science, data science, math, physics, or a related technical discipline; OR 4+ years of professional experience building software or simulation pipelines in lieu of a degree
• 1+ years of software development experience
• 1+ years of hands-on experience with at least one simulation domain (CFD, FEA, thermal, structural analysis, etc.)

Nice To Haves

• Experience with ANSA, Star-CCM+, OpenFOAM, Abaqus, OpenTD, OpenFOAM, CalculiX or similar commercial/in-house/open source simulation tools
• Strong proficiency scripting simulation APIs (especially ANSA Python API or Star-CCM+ automation)
• Demonstrated success building automated workflows that run thousands of simulations for dataset generation
• Understanding of Design of Experiments (DOE) and sampling techniques such as Latin Hypercube (LHS)
• Experience working in HPC environments with job schedulers such as Slurm or equivalent
• Familiarity with surrogate modeling concepts and the data requirements of neural operators, FNOs, physics-informed ML, or similar models
• Familiarity with deep learning and preparing data for ML workflows
• Proficiency with Python for scientific computing and automation
• Experience developing on Linux systems
• Strong understanding of version control, testing, continuous integration, build, deployment, and monitoring
• Good understanding of statistics, numerical methods, and core engineering simulation techniques

Responsibilities

• Develop and maintain automated simulation pipelines that generate training datasets for AI surrogate models at scale
• Partner with domain engineers to identify bottlenecks and build custom tools that improve productivity and reduce manual effort
• Create and optimize scripts using APIs such as ANSA Python API, Star-CCM+ Java/Python macros, OpenTD, Abaqus, or equivalent tools
• Build parametric workflows for geometry variation, automated meshing, batch simulation execution, and result extraction
• Orchestrate large-scale simulation campaigns on HPC clusters using job schedulers and workflow managers
• Collaborate closely with ML engineers to understand dataset requirements and iteratively improve data quality and diversity
• Implement data management, cleaning, metadata tagging, and versioned storage of simulation results
• Develop general automation tools and scripts to accelerate engineering workflows across simulation, analysis, design, and testing tasks
• Deep dive into engineering physics domains to ensure simulation setups are accurate, robust, and efficient for surrogate training
• Integrate simulation tools with version control, CI/CD pipelines, and monitoring systems for reproducible datasets
• Stay current with advances in simulation automation, meshing technology, and best practices for ML-ready datasets

Benefits

• Comprehensive medical, vision, and dental coverage
• Access to a 401(k) retirement plan
• Short and long-term disability insurance
• Life insurance
• Paid parental leave
• Various other discounts and perks
• 3 weeks of paid vacation
• 10 or more paid holidays per year
• Paid sick leave
• Long-term incentives, in the form of company stock, stock options, or long-term cash awards
• Potential discretionary bonuses
• Ability to purchase additional stock at a discount through an Employee Stock Purchase Plan

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level