Senior FEA Engineer

PhysicsX
United Kingdom
3 weeks ago
Seniority
Senior
Posted
23 Mar 2026 (3 weeks ago)

About us

PhysicsX is a deep-tech company with roots in numerical physics and Formula One, dedicated to accelerating hardware innovation at the speed of software. We are building an AI-driven simulation software stack for engineering and manufacturing across advanced industries. By enabling high-fidelity, multi-physics simulation through AI inference across the entire engineering lifecycle, PhysicsX unlocks new levels of optimization and automation in design, manufacturing, and operations — empowering engineers to push the boundaries of possibility. Our customers include leading innovators in Aerospace & Defense, Materials, Energy, Semiconductors, and Automotive.

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.

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.

Our delivery teams drive innovation to turn AI models into practical solutions - read our blog to learn more about how you’ll contribute to this exciting journey! We value diversity and are committed to equal employment opportunity regardless of sex, race, religion, ethnicity, nationality, disability, age, sexual orientation or gender identity. We strongly encourage individuals from groups traditionally underrepresented in tech to apply. To help make a change, we sponsor bright women from disadvantaged backgrounds through their university degrees in science and mathematics. We collect diversity and inclusion data solely for the purpose of monitoring the effectiveness of our equal opportunities policies and ensuring compliance with UK employment and equality legislation. This information is confidential, used only in aggregate form, and will not influence the outcome of your application.

Related Jobs

View all jobs

Senior Simulation Data Engineer

PhysicsX London, United Kingdom

Senior Machine Learning Engineer

PhysicsX London, United Kingdom

Senior Machine Learning Engineer

PhysicsX United Kingdom

Principal Machine Learning Engineer

PhysicsX United Kingdom

Senior DevOps Engineer

Quantexa London, United Kingdom
Hybrid

Senior AI/ML Engineer

Platform Recruitment London, United Kingdom
£90,000 – £120,000 pa

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Where to Advertise Machine Learning Jobs in the UK (2026 Guide)

Advertising machine learning jobs in the UK requires a different approach to most technical hiring. The candidate pool is small, highly specialised and in demand across AI labs, financial services, healthcare, autonomous systems and consumer technology simultaneously. Machine learning engineers and researchers move between roles through professional networks, conference communities and specialist platforms — not general job boards where ML roles compete with unrelated software engineering positions for the same audience. This guide, published by MachineLearningJobs.co.uk, covers where to advertise machine learning roles in the UK in 2026, how the main platforms compare, what employers should expect to pay, and what the data says about hiring across different role types.

New Machine Learning Employers to Watch in 2026: UK and Global Companies Driving ML Innovation

Machine learning (ML) has transitioned from a specialised field into a core business capability. In 2026, organisations across healthcare, finance, robotics, autonomous systems, natural language processing, and analytics are expanding their machine learning teams to build scalable intelligent products and services. For professionals exploring opportunities on www.MachineLearningJobs.co.uk , understanding the companies that are scaling, winning investment, or securing high‑impact contracts is crucial. This article highlights the new and high‑growth machine learning employers to watch in 2026, focusing on UK innovators, international firms with significant UK presence, and global platforms investing in machine learning talent locally.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.