Cfd Engineer

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 are 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…