AI Researcher

present of AI; Greater London; Hybrid; GBP 90,000 - 130,000; Full time

Job summary

BeyondMath seeks a senior researcher to design and train foundation-style physics models that accelerate engineering simulations. You will work on geometry handling, large-scale training, and fast inference for industrial customers, shipping models rather than keeping them in the lab.

You will mentor teammates, establish research standards, and ensure results obey physical laws while delivering reliable production-ready solutions.

Qualifications

• Several years of ML research with a strong publication record.
• Deep expertise in PyTorch or JAX for physics-informed modeling.
• Experience training models across multiple GPUs and shipping to production.

Responsibilities

• Design and train new foundation-style physics models.
• Ensure models run reliably inside a real product.
• Mentor others and set research standards.

Skills

PyTorch

JAX

Distributed GPUs

Physics-ML

FNO

PINNs

TensorFlow

C++

CUDA

Model deployment

Tools

CUDA

GPU clusters

Job description

The role is about designing and training new neural network models that solve the equations behind airflow and other physical behavior, then getting them running reliably inside a real product. A strong candidate has several years of research experience, a solid publication record, and deep skill in a framework like PyTorch or JAX. They also bring experience training models across many GPUs and the habit of shipping work rather than leaving it in the lab.

How it fits the AI era

This job sits at the center of the AI shift in engineering, since the models themselves replace slow simulation runs with much faster predictions. Tools that write code and speed up experiments can help with routine implementation, but choosing what to research, checking that results obey physical laws, and judging whether a prediction can be trusted still take human expertise. Mentoring others and setting research standards are also very human parts of the work.

How it builds AI experience

Someone in this role would gain hands-on experience building foundation-style models for physics, including geometry handling, large-scale training and fast inference for engineering customers in automotive, aerospace and energy. To build toward it, a candidate could reproduce work on operator learning or graph networks for fluid problems and test it on public flow datasets. Writing up how well the model holds up against a trusted solver shows the right mindset.

Worth showing

• Published ML research at top venues
• PyTorch or JAX custom models
• Distributed GPU training experience
• Models shipped to production
• Physics-ML such as FNO or PINNs

This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.

About BeyondMath

Foundational physics artificial intelligence platform accelerating engineering simulation and multi-physics aerodynamic design optimization.

Tools mentioned: deep learning, C++, PyTorch, TensorFlow, CUDA