Skills this job asks for
ExcelPythonJavaMachine learningDeep learning
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Sponsorship verdict
No sponsorship evidence yet
No government record and no wording either way. Not a refusal: ask the recruiter.
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The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
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Can’t check pay against the visa rulesNo salary stated. Australia Skills in Demand (482) visa needs at least A$79,423 a year.
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Confirmed live todayWhen a source last listed this job as open.
Details and sources
- Can’t check pay against the visa rules
- No salary stated. Australia Skills in Demand (482) visa needs at least A$79,423 a year. Source: https://www.legislation.gov.au/F2026L00874/asmade/2026-06-30/text/original/epub/OEBPS/document_1/document_1.html, rules effective 2026-07-01.
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Before you apply
Sponsorship unknown
No register record and no sponsorship wording in the posting. Worth asking the employer before investing significant time.
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About the role
About us
Re-architecting Engineering for the Age of Intelligence
PhysicsX is the physics AI company for industrials. The company’s mission is to accelerate hardware innovation by overhauling what industrial engineering and manufacturing look like today. PhysicsX is building a new simulation software stack to deliver deep physics AI enablement across the entire engineering lifecycle. The company partners with leading organisations in aerospace & defence, automotive, semiconductors, materials, and energy & renewables, supporting them on some of their most critical and complex challenges. PhysicsX is headquartered in the United Kingdom, with offices in London, New York, and Singapore and an expanding presence in the Bay Area.
Who we’re looking for
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.
What you'll do
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.
As the role evolves, there are exciting opportunities for growth as an Individual Contributor (IC) or a Team Lead (TL), especially if you’re driven to take ownership of more complex projects and lead the direction of future solutions.
Our delivery teams drive innovation to turn AI models into practical solutions - re
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