CFD Engineer

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.

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. You’ll play a crucial role in delivering high-fidelity simulations by: • 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. • 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! https://www.physicsx.ai/newsroom/how-delivery-makes-physical-ai-work-in-the-real-world What we offer Build what actually matters Help shape an AI-native engineering company at a formative stage, tackling problems that genuinely matter for industry and society. This is work with real-world impact - and something you can be proud to stand behind. Learn alongside exceptional people Work with a high-caliber, collaborative team of engineers, scientists, and operators who care deeply about doing great work, and about helping each other get better. We come from diverse backgrounds, but we share a commitment to operating at the highest level and addressing some of the most complex challenges out there. If you’re ambitious, thoughtful, and driven by impact, you’ll feel at home. Influence over hierarchy We operate with a flat structure: good ideas win - wherever they come from. Questioning assumptions and challenging the status quo isn’t just welcomed, it’s expected. Sustainable pace, long-term ambition Building meaningful technology is a marathon, not a sprint. We believe in balancing focused, ambitious work with a life beyond it. Our hybrid model blends time together in our New York office with work-from-home days, giving you the flexibility to work sustainably while staying connected in person. And it doesn’t stop there … 🚀 Equity options - share meaningfully in the company you’re helping to build. 💰 5% contribution to 401(k) - build long-term security with a strong retirement plan. 🍽️ Free team lunch 1x/week - good food, great company, and space to connect. 🏥 Private health insurance – comprehensive cover for you, offering total peace of mind. 👶 Enhanced parental leave – 3 months full pay paternity and 6 months full pay m