Asks for

The ad says“geometric deep learning and 3D vision over point-cloud, mesh, voxel or implicit-field data, including sparse convolution and sparse attention;”
The ad says“generative models for geometry — VAEs, diffusion and flow matching, autoregressive latent models, scaled to large datasets;”
The ad says“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.”
The ad says“generative models for geometry — VAEs, diffusion and flow matching, autoregressive latent models, scaled to large datasets;”
The ad says“generative models for geometry — VAEs, diffusion and flow matching, autoregressive latent models, scaled to large datasets;”
The ad says“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:”
The ad says“geometric deep learning and 3D vision over point-cloud, mesh, voxel or implicit-field data, including sparse convolution and sparse attention;”
The ad says“geometric deep learning and 3D vision over point-cloud, mesh, voxel or implicit-field data, including sparse convolution and sparse attention;”
The ad says“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:”
The ad says“geometric deep learning and 3D vision over point-cloud, mesh, voxel or implicit-field data, including sparse convolution and sparse attention;”
The ad says“neural fields and shape autoencoders.”
The ad says“geometric deep learning and 3D vision over point-cloud, mesh, voxel or implicit-field data, including sparse convolution and sparse attention;”
The ad says“Enthusiasm for deep learning and probabilistic methods applied to science and engineering.”
The ad says“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.”
The ad says“neural fields and shape autoencoders.”
The ad says“geometric deep learning and 3D vision over point-cloud, mesh, voxel or implicit-field data, including sparse convolution and sparse attention;”
The ad says“geometric deep learning and 3D vision over point-cloud, mesh, voxel or implicit-field data, including sparse convolution and sparse attention;”
The ad says“generative models for geometry — VAEs, diffusion and flow matching, autoregressive latent models, scaled to large datasets;”
The ad says“geometric deep learning and 3D vision over point-cloud, mesh, voxel or implicit-field data, including sparse convolution and sparse attention;”

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.

Note: We are currently recruiting for multiple levels and positions, however please only apply for the role that best aligns with your skillset and career goals.

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.

What you bring to the table

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:
1. generative models for geometry — VAEs, diffusion and flow matching, autoregressive latent models, scaled to large datasets;
2. geometric deep learning and 3D vision over point-cloud, mesh, voxel or implicit-field data, including sparse convolution and sparse attention;
3. 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..

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