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PhysicsX is a physics AI company for industrials, building simulation software that combines deep physics modeling with AI to accelerate hardware innovation. The company partners with leading organizations in aerospace & defense, automotive, semiconductors, materials, and energy & renewables.
You will join the Perth team at its founding stage, working alongside Data Scientists, Machine Learning Engineers, and customers to define and solve complex engineering challenges. This is a rare opportunity to help shape a regional team from the ground up with significant autonomy.
Key responsibilities include:
- Building CFD models from geometry cleanup and meshing through simulation and post-processing for simple to moderately complex cases, with guidance from senior engineers on difficult aspects
- Adapting parametric CAD models (NX or CATIA) and contributing to simulation pipeline automation for design optimization and design-of-experiments studies
- Supporting customer-facing work by preparing and presenting results clearly, progressing toward independent delivery
- Working at the intersection of CAE and Data Science to generate simulation datasets for training Machine/Deep Learning models, and understanding how data sampling choices affect model accuracy and cost
- Using Flux (the company's cloud platform) and on-premise HPC resources to run simulations, applying smarter meshing and setup choices to improve performance
- Learning and applying engineering best practices to support Deep Learning surrogate development
- Contributing to collaboration and shared learning within the Simulation Engineering guild, including documentation and onboarding support
The role offers growth opportunities as an Individual Contributor or Team Lead as you take ownership of more complex projects.
PhysicsX operates with a flat structure where good ideas win regardless of hierarchy. The company emphasizes sustainable pace, balancing ambitious work with life outside work. The Perth role is described as hybrid, blending time in office with work-from-home flexibility.
REQUIREMENTS:
- Growing foundation in fluid mechanics, heat transfer, and multiphase modeling
- Working knowledge of at least one of: Star-CCM+, OpenFOAM, or Fluent
- Comfortable using built-in automation features to support scalable workflows
- Exposure to parametric CAD modeling (NX or CATIA) is advantageous
- Coding in Python/Java or demonstrated ability to pick up new tools and languages quickly
- Ability to apply core engineering principles to real-world problems under guidance from senior colleagues
- Problem-solving mindset with passion for creating practical solutions that enable better engineering decisions
