
BeyondMath provides an AI-driven Generative Physics platform that simulates and optimizes complex physical systems to compress engineering design cycles from months to minutes. It uses a foundational…

BeyondMath provides an AI-driven Generative Physics platform that simulates and optimizes complex physical systems to compress engineering design cycles from months to minutes. It uses a foundational…
What they do: Generative physics AI platform for high‑speed engineering‑grade simulation and optimization (CFD, multiphysics)
Headquarters / year: UK (Cambridge/London), founded 2022
Product delivery: SaaS or licensing for simulation, design exploration, and component optimization
Performance claim: Up to 1,000x speedups; full‑car transient aerodynamics <100 seconds
Aerospace, automotive, motorsport (Formula 1), energy, medical devices
Funding (reported): Seed round(s) with reported totals including $8.5M and company materials referencing $18.5M
Engineering simulation and design optimization for multiphysics problems (fluid, thermal, structural), reducing computational time and physical prototyping.
2022
Software Development
8500000
Reported by third‑party profiles as an $8.5M seed round with participation from Insight Partners and InMotion Ventures.
18500000
Company materials reference a $18.5M seed total and a $10M seed extension led by Cambridge Innovation Capital.
“Backed by venture investors including Cambridge Innovation Capital, UP.Partners, Insight Partners, and InMotion Ventures”
About BeyondMath
BeyondMath is a pioneering startup, backed by top-tier VCs, on a mission to reshape the frontiers of engineering through Foundational AI models for Physics . We are replacing traditional, slow and expensive simulation methods with AI that rivals accuracy at orders of magnitude higher speed.
We are moving beyond the "generic AI" hype to solve the world’s hardest physical engineering challenges in automotive, aerospace, and energy.
The Role
As a Machine Learning Engineer, you’ll play a central role in advancing our Generative Physics simulation platform. You’ll work at the intersection of ML research and engineering—contributing to core model development, shaping model architecture, and delivering performant systems that integrate seamlessly into our real-world design optimization workflows.
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You'll work closely with our ML research team, software engineers, and industry partners to deploy robust, scalable models that deliver real-world impact.
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