At a Glance
- Tasks: Lead AI research to revolutionise engineering with cutting-edge Physics models.
- Company: Pioneering startup backed by top-tier VCs, focused on AI for Physics.
- Benefits: Full ownership of projects, high impact work, and collaboration with elite teams.
- Other info: Dynamic environment with opportunities to publish influential research and mentor junior talent.
- Why this job: Shape the future of sustainable energy and efficient transport through innovative AI solutions.
- Qualifications: PhD/MSc in relevant fields and 5+ years in AI/ML research.
The predicted salary is between 80000 - 100000 £ per year.
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.
We are looking for a Senior AI Researcher who balances scientific curiosity with the engineering discipline required to see models thrive in production environments.
The Role
As a Senior AI Researcher, you will be a core architect of our technical roadmap. This is not a "siloed" research role; you will lead the transition from theoretical breakthroughs in Physics based Deep Learning to robust, scalable systems used by world-class engineers. You will have the creative freedom to set research agendas while ensuring our models remain grounded in physical reality and industrial-scale performance.
Key Responsibilities
- Architect Physics-AI Foundations: Lead the research and development of novel ML architectures (e.g., Transformers, GNNs, or Diffusion models) designed specifically to solve complex partial differential equations (PDEs) including aerodynamic simulations.
- Bridge Research & Production: Translate high-level mathematical concepts into clean, high-performance code. You won’t just "throw models over the wall"; you will ensure they are optimized for inference and integrated into our production design platform.
- Advance Geometry Representation: Pioneer new ways to represent complex geometric design variations for efficient use in deep learning models.
- Strategic Leadership: Mentor junior researchers and engineers. Help define our internal research standards, reproducibility pipelines, and high-performance compute (HPC) infrastructure requirements.
- External Impact: Represent BeyondMath in the global AI community. Publish influential research at top-tier conferences (NeurIPS, ICML, ICLR) and position the company as the leader in "AI for Physics."
- Cross-Functional Collaboration: Partner with CFD specialists and software engineers to ensure our models respect physical constraints while maintaining the speed advantages of neural networks.
About You
You are a rare hybrid: a scientist who loves the elegance of a theorem, but an engineer who gets a thrill from seeing a model successfully optimize a real-world turbine or airframe. You thrive in the ambiguity of a "greenfield" opportunity and have the grit to solve problems where no textbook solution exists.
Essential Requirements
- PhD or MSc in Computer Science, Physics, Mathematics, or a related quantitative field.
- 5+ years of post-grad experience in AI/ML research, with a demonstrable track record of models made it from the lab into production environments.
- Deep Technical Mastery: Expert-level proficiency in PyTorch, JAX, or TensorFlow, with a focus on building custom layers, loss functions, and optimization loops.
- Published Excellence: A strong record of high-quality publications in top-tier venues (e.g., NeurIPS, ICML, CVPR, or physics-specific AI journals).
- Systems Thinking: Experience with scalable training infrastructure, including distributed training across GPU clusters and data pipeline automation.
Highly Desirable
- Physics-ML Expertise: Experience with Physics-Informed Neural Networks (PINNs), Operator Learning (DeepONet/FNO), or Equivariant Neural Networks.
- Domain Knowledge: Familiarity with Aerodynamics, Fluid Dynamics, or Structural Mechanics.
- Engineering Rigor: Familiarity with C++, CUDA for low-level model optimization.
Why Join Us?
- Full Ownership: You will have a direct seat at the table in shaping the future of a company redefining an entire industry.
- High Impact: Your work will directly accelerate the transition to sustainable energy and more efficient transport.
- Elite Team: Work alongside veterans from world-leading AI labs and engineering firms in a culture of "impact with integrity."
AI Researcher in London employer: BeyondMath
BeyondMath is an exceptional employer for AI Researchers, offering a unique opportunity to work at the forefront of Physics-based AI innovation. With a culture that fosters creativity and collaboration, employees enjoy full ownership of their projects while being part of an elite team comprised of industry veterans. The company prioritises professional growth, providing avenues for impactful research and the chance to shape the future of sustainable energy and transport.
StudySmarter Expert Advice🤫
We think this is how you could land AI Researcher in London
✨Tip Number 1
Network like a pro! Get out there and connect with folks in the AI and engineering space. Attend meetups, conferences, or even online webinars. You never know who might have the inside scoop on job openings or can put in a good word for you!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those that bridge theory and practical applications. This is your chance to demonstrate how your research translates into real-world solutions, which is exactly what BeyondMath is after.
✨Tip Number 3
Don’t just apply; engage! When you find a role that excites you, reach out to current employees on LinkedIn. Ask them about their experiences and share your enthusiasm for the company’s mission. This personal touch can make a big difference.
✨Tip Number 4
Keep learning and adapting! The AI field is always evolving, so stay updated with the latest trends and technologies. Join online courses or workshops to enhance your skills, and don’t forget to apply through our website for the best chances!
We think you need these skills to ace AI Researcher in London
Some tips for your application 🫡
Show Your Passion for AI:When writing your application, let your enthusiasm for AI and physics shine through! We want to see how your curiosity drives your research and how you envision applying it in real-world scenarios.
Tailor Your CV and Cover Letter:Make sure to customise your CV and cover letter to highlight your relevant experience and skills. We love seeing how your background aligns with our mission at BeyondMath, so don’t hold back on showcasing your achievements!
Highlight Your Publications:If you've got a strong record of publications, make sure to mention them! We’re keen on candidates who have made an impact in the AI community, so include links or references to your best work.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity to join our team!
How to prepare for a job interview at BeyondMath
✨Know Your Physics and AI Inside Out
Make sure you brush up on your knowledge of physics, especially in areas like aerodynamics and fluid dynamics. Be ready to discuss how your expertise in AI can be applied to solve complex physical problems, as this will show your understanding of the role's requirements.
✨Showcase Your Research Impact
Prepare to talk about your previous research and how it transitioned from theory to production. Highlight specific projects where your models made a tangible impact, especially if they were published in top-tier conferences like NeurIPS or ICML.
✨Demonstrate Your Coding Skills
Since you'll be translating high-level concepts into clean code, be ready to discuss your experience with frameworks like PyTorch or TensorFlow. Consider bringing examples of custom layers or optimization loops you've built, as practical demonstrations can really impress.
✨Emphasise Collaboration and Leadership
This role involves mentoring and cross-functional collaboration, so be prepared to share experiences where you've led teams or worked closely with engineers. Discuss how you ensure that models respect physical constraints while maintaining performance, showcasing your systems thinking.
