.jpg)
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.
You're a problem solver and builder, passionate about creating practical solutions that help customers make better engineering decisions. You can grasp and apply advanced engineering concepts across multiple industries, and you excel at working directly with internal and external stakeholders, often on-site, to develop high-fidelity simulation models that feed into AI tools that are both useful and used.
You bring deep expertise in fluid mechanics, heat transfer and multiphase modelling within bioprocess and chemical engineering environments. You are highly proficient in at least one of Star-CCM+, OpenFOAM or Fluent, and experienced modelling the complex flow behaviour that arises in industrial bioprocess systems. You are adept at automating these tools to create scalable optimisation workflows. Experience in parametric CAD modelling (NX or CATIA) and coding in Python (or the ability to pick up new programming languages quickly) is an advantage.
- Develop multiphase flow models for gas-liquid, solid-liquid, VOF, Euler-Euler/Euler-Lagrange approaches) across a range of industrial and process engineering applications
- Model non-Newtonian and complex fluid rheology modelling, including high-viscosity, shear-thinning or particle-laden flow regimes
- Reactive and coupled-physics flow modelling: link CFD with reaction kinetics, heat transfer or domain-specific process models (e.g. biokinetic, combustion or electrochemical frameworks)
- Own meshing generation and strategy for complex industrial geometries, including rotating machinery, internal flow passages, spargers and free-surface interfaces
- Build robust parametric CAD models (NX, CATIA or equivalent) tightly coupled with simulation pipelines, enabling automated design optimisation and DoE studies
- Multi-physics model development end-to-end: geometry clean-up, meshing, solver setup, post-processing and experimental data integration for model validation
- Scale-up/scale-down methodology: translate small-scale experimental data to full-scale CFD models and iterating model fidelity against physical measurements
- HPC: job scheduling, MPI-based distributed computing, GPU acceleration and performance tuning for large-mesh transient simulations on cloud (Flux) and on-premise resources
- Surrogate modelling and ML-CFD coupling: build reduced-order or AI surrogate models from high-fidelity CFD data to support design space exploration and process optimisation
- Data pipeline literacy: structure and curate CFD output datasets for downstream AI/ML training, active learning and Pareto optimisation workflows; applying data sampling techniques (LHS, quasi-random, adaptive) to efficiently cover design space
- Experimental validation workflows: compare simulation predictions against physical test data, interpreting discrepancies and systematically improving model fidelity
- Customer-facing delivery: partner with clients to scope and address complex engineering challenges via CAE and AI solutions; communicating results clearly, recommending actionable next steps and balancing accuracy with efficiency under commercial deadlines
- Proficiency in CFD solvers across open-source OpenFOAM and commercial platforms comes as a plus (Star-CCM+, Fluent or equivalent), including custom solver and boundary condition development.
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 offerBuild 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.
Skills Required
- 3–5 years of industry experience in a commercial, non-research environment after a Master’s degree or PhD
- Deep expertise in fluid mechanics, heat transfer, and multiphase modeling
- High proficiency in at least one of Star-CCM+, OpenFOAM, or Fluent
- Ability to independently set up CFD simulations, interpret complex results, and apply engineering judgment
- Experience building multiphysics models, including geometry cleanup, meshing, simulation, post-processing, and model validation
- Experience working with customers to solve complex engineering challenges and communicate results clearly
- Experience with parametric CAD modeling using NX or CATIA
- Programming experience in Python or Java
- Experience automating simulation tools and creating optimization or design-of-experiments workflows
- Experience generating simulation datasets for machine learning or deep learning models
- Willingness to travel globally up to 2–3 weeks per quarter
What We Do
PhysicsX is a deep-tech company of scientists and engineers, developing machine learning applications to accelerate physics simulations and enable a new frontier of optimization opportunities in design and engineering. Born out of numerical physics, we help our customers radically improve their concepts and designs, transform their engineering processes and drive operational product performance. We do this in some of the most advanced and important industries of our time – including Space, Aerospace, Medical Devices, Additive Manufacturing, Electric Vehicles, Motorsport, and Renewables. Our work creates positive impact for society, be it by improving the design of artificial hearts, reducing CO2 emissions from aircraft and road vehicles, and increasing the performance of wind turbines. We are currently recruiting for multiple positions, however please only apply for the role that best aligns with your skillset and career goals. We do not currently offer work experience
.png)
.jpg)
