Senior CFD Engineer - Multiphase

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

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.

What you'll do

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 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.

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 employment and equality legislation. This information is confidential, used only in aggregate form, and will not influence the outcome of your application.