CFD Engineer

PhysicsX Ltd.; Greater London; On-site; AUD 100,000 - 150,000; Full time

Job summary

PhysicsX Ltd. is seeking a CFD Engineer (Delivery) to build multi-physics CAE models and run simulations on-site for real-world industrial challenges. You will collaborate with data scientists and customers to define engineering problems and deliver actionable results.

The role emphasizes automation of CFD workflows, coupling CAD models with simulations, and generating training data for ML surrogates. Prior 3-5 years in industry R&D or delivery environments is preferred.

Qualifications

• 3-5 years industry CFD experience (post Masters or PhD)
• Deep knowledge of fluid mechanics, heat transfer, and multiphase modelling.
• Experience automating CFD tools to create scalable optimisation workflows.

Responsibilities

• Independently build complex multi-physics models from geometry clean-up to simulating and post-processing complex real-world phenomena.
• Build robust parametric CAD models (NX or CATIA) coupled with simulation pipeline automation for advanced design optimization and DoE studies.
• Partner with customers to address their most complex engineering challenges through advanced CAE & AI solutions; present results clearly and balance accuracy with efficiency.
• Generate high-quality simulation datasets for training Machine/Deep Learning models by leveraging data sampling to capture design space efficiently.
• Accelerate high-fidelity modelling by using Flux (cloud) and on-premise HPC resources to achieve performance gains.
• Continuously improving CFD setups to support the development of Deep Learning surrogates.

Skills

CFD modelling

Python/Java coding

Industry experience

On-site engagement

Automation workflows

Education

Master's degree or PhD in a relevant field

Tools

Star-CCM+

OpenFOAM

Fluent

Job description

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

As a CFD Engineer (Delivery), you are 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. You bring deep expertise in fluid mechanics, heat transfer, and multiphase modelling, with a solid ability to apply fundamental knowledge to real-world phenomena across a wide range of engineering applications. Highly proficient in at least one of Star-CCM+, OpenFOAM, or Fluent and adept at automating these tools to create scalable optimisation workflows that drive impactful results. Experience in parametric CAD modelling (NX or CATIA) and coding in Python/Java, (or the ability to quickly learn programming languages), is an advantage. With 3-5 years of industry experience (post Masters or PhD) in a commercial, non-research environment, you’re ready to hit the ground running. You’re confident setting up CFD simulations independently, interpreting complex results with depth, and making informed decisions based on solid engineering judgement.

What you’ll do

In this role, you’ll work closely with our Data Scientists, Machine Learning Engineers, and customers to understand and define the engineering challenges we are solving.

• Independently building complex multi-physics models from geometry clean-up and meshing, to simulating and post-processing complex real-world phenomena, integrating experimental data for model validation.
• Building robust parametric CAD models (NX or CATIA) coupled with simulation pipeline automation, for executing advanced design optimization and DoE studies.
• Partner with customers to address their most complex engineering challenges through advanced CAE & AI solutions; present results clearly, recommend actionable next steps, and balance accuracy with efficiency under tight deadlines.
• Working at the intersection of CAE and Data Science to generate high-quality simulation datasets for training Machine/Deep Learning models. Leveraging data sampling techniques to efficiently capture the design space, reduce computational cost, and enhance model accuracy.
• Accelerate high-fidelity modelling by using Flux (our cloud platform) and on-premise HPC resources, going beyond smart meshing and model setup to achieve real performance gains.
• Continuously improving engineering best practices, adapting CFD model setups and outputs to support the development of Deep Learning surrogates.