You're a problem solver and a builder, who is passionate about creating practical solutions that enable customers to make better engineering decisions. You are someone who can grasp and apply advanced engineering concepts across multiple industries, and you excel at working directly with customers (often directly on-site) to build CAE models that are integrated into AI-tools that are both useful and used. 

We're building our Perth presence from the ground up. This is a rare opportunity to join at the founding stage of a regional team, with the autonomy to help define what this team becomes.

You bring a growing foundation in fluid mechanics, heat transfer, and multiphase modelling, with the ability to apply core engineering principles to real-world problems under guidance from more senior colleagues. You have working knowledge of at least one of Star-CCM+, OpenFOAM, or Fluent, and are comfortable using built-in automation features to support scalable workflows. Exposure to parametric CAD modelling (NX or CATIA) and coding in Python/Java - or a demonstrated ability to pick up new tools and languages quickly - is an advantage.

In this role, you'll work alongside our Data Scientists, Machine Learning Engineers, and (with support) our customers to help define and solve engineering challenges.

You'll contribute to delivering high-fidelity simulations by:

• Building models from geometry clean-up and meshing through to simulating and post-processing, for simple to moderately complex cases, with guidance from senior engineers on the more difficult aspects.
• Adapting and using parametric CAD models (NX or CATIA), and beginning to contribute to simulation pipeline automation for design optimisation and DoE studies.
• Supporting customer-facing work by helping prepare and present results clearly, under the direction of more senior team members, while building toward doing this independently.
• Working at the intersection of CAE and Data Science to help generate simulation datasets for training Machine/Deep Learning models, and developing your understanding of how data sampling choices affect model accuracy and cost.
• Using Flux (our cloud platform) and on-premise HPC resources to run simulations, and learning how to apply smarter meshing and setup choices to improve performance.
• Learning and applying engineering best practices, and helping adapt CFD model setups and outputs to support Deep Learning surrogate development.
• Contributing to a culture of collaboration and shared learning within the Simulation Engineering guild - for example, sharing what you learn, flagging gaps in documentation, or (at L2) starting to support onboarding of newer engineers.