Senior CFD Engineer - Multiphase

PhysicsX Ltd.; Greater London; Hybrid; GBP 70,000 - 100,000; Full time

Benefits offered by this job

Equity options

10% employer pension contribution

Free office lunches

Hybrid work model

Private medical insurance

Eye tests

Personal development

Employee Assistance Programme (EAP)

Bike2Work scheme

Octopus EV salary sacrifice

Job summary

PhysicsX Ltd. is seeking a skilled engineer to lead the development of high-fidelity multi-physics simulations, combining CFD-DEM with AI-driven workflows. You will automate pipelines, validate models, and collaborate with customers to deliver practical design insights.

Based in London with a hybrid work model, you will mentor junior engineers, drive DoE studies, and travel internationally to work on-site with clients in diverse industries.

Qualifications

• 3–5 years post-graduate experience in industry (not research).
• Python scripting and simulation automation experience.
• DoE, surrogate modelling, and design space exploration.
• Parametric CAD modelling (NX or CATIA advantageous).
• Strong written and verbal communication with technical and non-technical audiences.

Responsibilities

• Independently build complex multi-physics models from geometry clean-up to simulation and validation, spanning single-phase, multiphase, and CFD-DEM regimes.
• Develop and validate DEM models of granular and particle-laden systems including fluidised beds and pneumatic conveying.
• Build robust parametric CAD models and automate simulation pipelines for design optimisation and DoE studies.
• Partner with customers to address engineering challenges, present results, and propose actionable next steps.
• Work at the intersection of CAE and data science to create high-quality datasets for ML surrogates.
• Leverage Flux and HPC resources to accelerate high-fidelity modelling at scale.
• Improve engineering best practices and mentor junior colleagues; travel globally 2–3 weeks per quarter to work on-site with customers.

Skills

Problem solving

Engineering judgement

Strong communication

Project leadership

Education

MEng, MSc, or PhD in mechanical, chemical, or process engineering

Tools

Star-CCM+

OpenFOAM

Fluent

EDEM

LIGGGHTS

Rocky

NX

CATIA

Python

Job description

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

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 customers, 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, including particle-laden flows, fluidised beds, and coupled CFD-DEM. You are highly proficient in at least one of Star-CCM+, OpenFOAM, or Fluent, and experienced with DEM codes such as EDEM, LIGGGHTS, or Rocky. 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.

With 3–5 years of industry experience (post-MEng, MSc, or PhD) in a commercial, non-research environment, you are ready to hit the ground running. You are confident setting up simulations independently, interpreting complex results with rigour, and making sound decisions grounded in solid engineering judgement.

What you’ll do

• Independently build complex multi-physics models, from geometry clean-up and meshing through to simulation, post-processing, and integration of experimental data for validation, spanning single-phase, multiphase, and coupled CFD-DEM regimes.
• Develop and validate DEM models of granular and particle-laden systems, including fluidised beds, pneumatic conveying, mills, aerosols, suspensions and coating processes, applying appropriate contact models, size distributions, and coupling strategies.
• Build robust parametric CAD models coupled with simulation pipeline automation to execute advanced design optimisation and DoE studies across large parameter spaces.
• Partner with customers to address complex engineering challenges through advanced simulation and AI solutions. Present results clearly, recommend actionable next steps, and balance accuracy with efficiency under real project deadlines.
• Work at the intersection of CAE and data science to generate high-quality simulation datasets for training machine learning and deep learning surrogate models. Apply smart sampling strategies to capture the design space efficiently and reduce computational cost.
• Leverage Flux (our cloud platform) and on-premise HPC resources to accelerate high-fidelity modelling, going beyond standard setup practices to achieve genuine performance gains at scale.
• Continuously improve engineering best practices, adapting simulation model setups and outputs to support the development of deep learning surrogates and physics-informed ML models.
• Combine project leadership with a commitment to mentoring junior colleagues, contributing to a culture of collaboration, rigour, and shared growth.
• Travel globally (North America, Europe, Asia, Oceania) up to 2–3 weeks per quarter to work on-site with customers building solutions together.

What we’re looking for

Core simulation skills

• Multiphase CFD: VOF, Euler–Euler, Euler–Lagrange, or mixture models
• DEM modelling: EDEM, Rocky, LIGGGHTS, or equivalent
• Coupled CFD–DEM workflows for particle–fluid systems
• Heat and mass transfer modelling
• Reacting flows and phase-change processes (advantageous)
• Proficiency in Star-CCM+, OpenFOAM, or Fluent

Engineering and workflow

• 3–5 years post-graduate experience in industry (not research)
• MEng, MSc, or PhD in mechanical, chemical, or process engineering
• Python scripting and simulation automation
• DoE, surrogate modelling, and design space exploration
• Parametric CAD modelling (NX or CATIA advantageous)
• Strong written and verbal communication with technical and non-technical audiences

Please note, this role is based in London, working 2-3 days per week in our central office.

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!

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 Shoreditch 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.

10% employer pension contribution - because investing in future matters.

Free office lunches - to keep you energised and focused.

Enhanced parental leave - 3 months full pay paternity and 6 months full pay maternity leave, to provide extra flexibility during the moments that matter most.

YellowNest nursery scheme - to help working parents manage childcare costs.

25 days of Annual Leave (+ Public Holidays) - because taking time to rest matters.

Private medical insurance - 100% employee cover, giving you complete peace of mind.

Wellhub Subscription - gain access to thousands of gyms, classes and wellness apps, supporting both physical and mental wellbeing.

Eye tests - because good work depends on good health.

Personal development - dedicated support for learning, development, and leveling up over time.

Employee Assistance Programme (EAP) - confidential wellbeing support, available whenever you need it.

Bike2Work scheme and Season ticket loan - to make getting to work easier and greener.

Octopus EV salary sacrifice - for a simpler, more sustainable way to drive electric.

Watch this space, we’re continuing to build this as we grow…

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.