Software Engineer, Engineering Simulation & Automation (Vehicle Engineering)
Core
Design, develop, and maintain scalable simulation automation pipelines to generate high-quality training datasets for AI surrogate models at scale, supporting engineering analysis, simulation, and mission operations for launch vehicles and spacecraft.
Role type
Software Engineer, Engineering Simulation & Automation
Builds
Automated simulation pipelines, custom scripts for geometry parameterization/meshing/solving/post-processing, and general automation tools for engineering workflows.
Domain
Aerospace engineering, vehicle engineering, AI/ML for engineering, simulation (CFD, FEA, thermal, structural)
Deliverable
production ML models
Required skills
Python for scientific computing and automation, simulation API scripting (ANSA, Star-CCM+, Abaqus, OpenTD), HPC workflow orchestration, data management and versioning, CI/CD integration, Linux development, statistical methods, numerical methods
Preferred skills
Experience with commercial/in-house simulation tools (ANSA, Star-CCM+, OpenFOAM, CalculiX), Design of Experiments (DOE) and sampling techniques (Latin Hypercube), surrogate modeling concepts (neural operators, FNOs), deep learning data preparation, job schedulers (Slurm)
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; 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
Seniority
Mid-level IC
