About us

Who We’re Looking For

As a Senior FEA 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.

We're building our Singapore 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 deep expertise in structural mechanics, materials science, and multi-physics 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 NASTRAN, ABAQUS or ANSYS Mechanical, adept at automating these tools to create scalable optimisation workflows that drive impactful results. Experience with open source FEA packages such as CalculiX or FEniCS would be highly advantageous. Proficiency in parametric CAD modelling (NX or CATIA) and coding in Python, (or the ability to quickly learn programming languages), is an advantage. Experience with explicit dynamics (LS-DYNA, Abaqus/Explicit) such as crash simulation is highly valued. Background in coupled simulations such as thermo-mechanical analysis and fluid-structure interaction modelling also is highly desirable.

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 FEA simulations independently, interpreting complex results with depth, and making informed decisions based on solid engineering judgement.

This Role

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.

You’ll play a crucial role in delivering high-fidelity simulations by:

• Independently building complex structural and 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 through scripting, for executing advanced design optimization and DoE studies.
• Partner with customers to address their most complex engineering challenges through advanced FEA & AI solutions; communicate 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 FEA model setups and outputs to support the development of Deep Learning surrogates.
• Documenting workflows and results comprehensively via technical reports and process documentation that enable knowledge transfer and support reproducible engineering methodologies
• Combining project leadership with a strong commitment to mentoring junior colleagues, contributing to a culture of collaboration, growth, and shared success.
• Traveling globally (North America, Europe, Asia, Oceania) up to 2–3 weeks per quarter to work side-by-side with customers in building solutions on-site.

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.