Software Engineer, Engineering Simulation & Automation (Vehicle Engineering)

Summary

SpaceX is actively developing technologies to enable human life on Mars. They are seeking a Software Engineer for the Engineering Simulation & Automation team, focusing on creating automated simulation tools and pipelines to enhance engineering for launch vehicles and spacecraft. The role involves designing and maintaining scalable simulation automation pipelines and collaborating with domain experts to improve engineering productivity.

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

Skills

• 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.)
• 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

Qualifications

Must Haves

• 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

Benefits

• Long-term incentives, in the form of company stock, stock options, or long-term cash awards
• Potential discretionary bonuses
• The ability to purchase additional stock at a discount through an Employee Stock Purchase Plan
• 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
• Various other discounts and perks
• 3 weeks of paid vacation
• 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