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PhysicsX jobs: what the physics AI boom means for UK ML engineers
PhysicsX has raised $300m and doubled its team. Here's what physics AI jobs involve, the skills they ask for and how to apply.
By Julie Fixter, Client Partnership Consultant, Future Tech Jobs
PhysicsX is one of the fastest-growing machine learning employers in the UK. In June 2026 the London company raised a $300 million Series C at a valuation of around $2.4 billion, and it says its team has grown to more than 300 people, roughly doubling in a year. If you work in machine learning, simulation or engineering, this is a company worth understanding, because the jobs it creates sit right where those three fields meet.
Here is what PhysicsX does, the roles it hires for, the skills its adverts ask for and how to give yourself the best chance of getting in.
What is PhysicsX?
PhysicsX is a physics AI company for industrial engineering. Its platform uses AI models that predict how a physical design will behave in seconds, so engineering teams can test far more design variants than traditional simulation allows.
The company describes itself as having roots in numerical physics and Formula One. Its customers work in advanced industries including:
Aerospace and defence
Semiconductors
Automotive
Energy
Materials and industrial machinery
Its headquarters is in Shoreditch, London, and it also has an office in New York.
Why PhysicsX is hiring now
The short answer is money and momentum. The Series C was led by Temasek, with M&G Investments and Intrepid Growth Partners joining existing backers including NVIDIA, Siemens, Applied Materials, Atomico and General Catalyst. The round was oversubscribed.
According to the announcement, PhysicsX will use the funding to grow globally, develop its platform and push frontier research, including larger pre-trained "Large Physics Models". The company also reported that its recognised revenue doubled year on year and its customer count more than doubled.
When a company grows revenue and headcount at that pace, it needs people across research, product and customer delivery at the same time. That is exactly the mix showing up in its adverts.
What jobs does PhysicsX hire for?
PhysicsX organises its work into four areas, and each one hires differently:
Research: builds the Large Physics Models and the methods behind them. Think research scientists and research-focused ML engineers.
Product: builds the platform that data scientists, ML engineers and simulation engineers use.
Delivery: embeds engineers and domain experts with customers to put the technology to work.
Operations: strategy, people, finance, marketing, legal and IT.
London-based roles advertised on Machine Learning Jobs show the range:
Research Scientist, Large Geometry Models: designing and training generative models over 3D geometry and checking them against real physics solvers.
Staff Machine Learning Software Engineer, Research: distributed multi-GPU training and scaling foundation models for science and engineering.
Senior Machine Learning Infrastructure Engineer, Research: running distributed training on NVIDIA DGX B200 systems and building serving infrastructure for pre-trained physics models.
CFD Engineer: building simulations and generating the datasets used to train ML models.
You can see every current opening on the PhysicsX company page.
What skills do PhysicsX jobs ask for?
The adverts are demanding, but they are also clear about what matters. Across the London roles, a few themes come up again and again.
For research roles, expect to need a PhD in computer science, machine learning, maths, physics, engineering or similar, hands-on PyTorch and CUDA work on 3D or geometric data, and publications at venues such as NeurIPS, ICML, ICLR or CVPR. Interest in generative models for geometry, neural fields or operator learning for PDEs is a plus.
For ML engineering and infrastructure roles, the focus moves to scale. The adverts mention distributed training (FSDP, DDP, NCCL), Kubernetes and Slurm on GPU clusters, Python and PyTorch, and solid software practice: testing, CI/CD, API design and MLOps.
For simulation roles, the CFD Engineer advert asks for a growing foundation in fluid mechanics, heat transfer and multiphase modelling, plus working knowledge of at least one of Star-CCM+, OpenFOAM or Fluent. Python is a nice-to-have, as is parametric CAD in NX or CATIA.
That last point matters. You do not have to be a machine learning specialist already to work in physics AI. Simulation engineers who generate training data and understand the physics are part of the core team.
Can CFD and simulation engineers move into machine learning?
Yes, and physics AI is one of the most natural routes. Companies in this space need people who understand meshes, solvers and boundary conditions as much as they need people who understand neural networks.
PhysicsX is not the only example. A current Knowledge Transfer Partnership between the University of Southampton and London deep-tech company Compute Maritime is hiring a Machine Learning Engineer for ship design and hydrodynamics, working on physics-informed generative AI. It asks for ML applied to physical systems alongside simulation experience such as STAR-CCM+.
If you are coming from simulation, it helps to:
Get comfortable with Python and PyTorch on your own engineering data.
Learn how surrogate models work and where they fail.
Show one project where you automated a simulation workflow or built a dataset for training.
Set up a job alert so you see physics AI roles the day they go live.
From the people side of hiring, I would add one thing. Specialists rarely move just for the tech stack. They move because they can see their existing expertise will be valued, not thrown away. Physics AI is one of the few fields where a background in fluids or structures is an advantage rather than something to explain away.
What is it like to apply to PhysicsX?
PhysicsX sets out its hiring process on its careers page. It has five stages:
An introduction interview.
A first problem-solving interview tailored to the role, such as pair programming, a take-home challenge or a presentation.
A second, deeper technical interview.
A final interview, often with senior team members.
An offer.
At least one interview is in person. The company says candidates who reach interview but do not progress should hear back within a few days with feedback.
Most staff work in the office two to three days a week. The careers page mentions relocation support for the UK and US and visa sponsorship in the UK where applicable. The Staff ML Software Engineer advert lists equity options, a 10% employer pension contribution, 25 days' annual leave plus public holidays and private medical insurance.
FAQ
Is PhysicsX a UK company?
Yes. PhysicsX is headquartered in Shoreditch, London, and also has an office in New York.
How much funding has PhysicsX raised?
PhysicsX announced a $300 million Series C in June 2026, led by Temasek, at a valuation of around $2.4 billion.
Do I need a PhD to work at PhysicsX?
Not for every role. Research scientist roles ask for a PhD, but engineering and simulation roles focus on industry experience and practical skills. Check each advert for its requirements.
What programming languages does PhysicsX use?
Python is the common thread, with PyTorch, NumPy, SciPy and Pandas appearing across adverts. Some research roles also mention JAX, CUDA and C/C++.
Where can I find PhysicsX jobs?
Current openings are listed on the PhysicsX page on Machine Learning Jobs, alongside other UK machine learning roles.
Find your next physics AI or machine learning role
Physics AI is one of the most exciting corners of UK machine learning right now, and it is hiring across research, engineering and simulation. Here is how to get ahead:
Browse machine learning jobs across the UK.
Set up a job alert for "machine learning engineer", "research scientist" or "simulation".
Upload your CV so employers can find you.
Create a free account to save jobs and apply faster.
See all openings on the PhysicsX company page.
About the author
Julie Fixter is a Client Partnership Consultant at Future Tech Jobs, helping employers and recruitment partners reach specialist talent across AI, quantum, robotics, semiconductors, space and the rest of the FTJ network. She spent seven years in sales at a UK software house before joining FTJ, and trained in psychodynamic counselling, which shapes how she thinks about what candidates actually want from a move. Based in Manchester. Connect with Julie on LinkedIn.
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