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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.
Geometry is at the heart of how we think about engineering. You will work at the frontier of 3D geometry representation and generation, with a focus on making both of these ready for engineering. You will also work on closing the coupling between geometry and physics, towards generating shapes that are driven by physical objectives.
What you will do- Own a research work-stream: set its technical direction, align priorities with internal and external stakeholders, and apply judgement and taste to drive progress.
- Design and train generative models over 3D geometry — representations, latent spaces, and the objectives that decide whether generated geometry is physically usable rather than merely plausible.
- Work against production numerical solvers, so model quality is measured in validated physics rather than proxy metrics.
- Work with our ML engineers, simulation engineers and customers to turn engineering challenges into mathematical formulations.
- Champion research directions valuable to the company, nurture more junior colleagues, and publish for both academic and non-academic audiences.
- PhD in CS, ML, mathematics, physics, engineering or a related field, with demonstrated expertise in generative modelling or learned representations of 3D geometry, covering one or more of:
- generative models for geometry — VAEs, diffusion and flow matching, autoregressive latent models, scaled to large datasets;
- geometric deep learning and 3D vision over point-cloud, mesh, voxel or implicit-field data, including sparse convolution and sparse attention;
- neural fields and shape autoencoders.
- Relevant experience in industry or a research group of comparable intensity, instrumental in: building models and pipelines in PyTorch/CUDA; bespoke problem settings involving geometric or 3D data; iterating on architecture and inductive bias; combining theoretical reasoning with empirical intuition; running experiment pipelines that produce comparable results.
- Ability to scope and deliver projects, with strong problem-solving skills.
- Enthusiasm for deep learning and probabilistic methods applied to science and engineering.
- Publications at premier conferences: SIGGRAPH, SIGGRAPH Asia, CVPR, ICCV, ECCV, NeurIPS, ICML or ICLR, and relevant journals: TOG, TPAMI, JMLR.
- Desirable: operator learning or probabilistic methods for PDEs; self-supervised pretraining for 3D vision and/or physics surrogacy; generative inverse design linking geometry to physics objectives..
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…
Skills Required
- PhD in computer science, machine learning, mathematics, physics, engineering, or a related field
- Demonstrated expertise in generative modeling or learned representations of 3D geometry
- Experience with generative models for geometry, including VAEs, diffusion, flow matching, or autoregressive latent models
- Experience with geometric deep learning and 3D vision involving point clouds, meshes, voxels, or implicit fields
- Experience with neural fields or shape autoencoders
- Industry or intensive research-group experience building models and pipelines in PyTorch and CUDA
- Experience working with geometric or 3D data and iterating on model architecture and inductive bias
- Ability to combine theoretical reasoning with empirical intuition and run comparable experiment pipelines
- Ability to scope and deliver projects with strong problem-solving skills
- Enthusiasm for deep learning and probabilistic methods applied to science and engineering
- Publications at premier conferences or relevant journals, such as SIGGRAPH, CVPR, NeurIPS, ICML, ICLR, TOG, TPAMI, or JMLR
- Experience with operator learning or probabilistic methods for PDEs
- Experience with self-supervised pretraining for 3D vision or physics surrogacy
- Experience with generative inverse design linking geometry to physics objectives
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
