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- Tasks: Lead AI research to revolutionise engineering with cutting-edge Physics models.
- Company: Pioneering startup backed by top-tier VCs, reshaping the future of engineering.
- Benefits: Full ownership of projects, high impact work, and collaboration with elite teams.
- Other info: Join a culture of integrity and innovation with excellent career growth.
- Why this job: Make a real difference in sustainable energy and efficient transport.
- 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 employer: BeyondMath
Contact Detail:
BeyondMath Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI Researcher
✨Tip Number 1
Network like a pro! Reach out to folks in the AI and physics communities, attend meetups, and engage on platforms like LinkedIn. 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. Highlight any models you've developed that made it into production – this is what BeyondMath is all about!
✨Tip Number 3
Prepare for interviews by brushing up on both technical and soft skills. Be ready to discuss your research, how you’ve tackled complex problems, and how you can contribute to a collaborative environment. Remember, they want to see your passion for AI and physics!
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who take the initiative to connect directly with us. Don’t hesitate – get your application in today!
We think you need these skills to ace AI Researcher
Some tips for your application 🫡
Show Your Passion for AI and Physics: 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 to real-world challenges. Make it personal and relatable!
Highlight Your Practical Experience: Don’t just list your qualifications; tell us about your hands-on experience in transitioning models from theory to production. We’re keen on seeing examples of your work that demonstrate your ability to bridge the gap between research and practical application.
Be Clear and Concise: While we love a good story, clarity is key! Make sure your application is well-structured and easy to read. Use bullet points where necessary to highlight your achievements and skills, so we can quickly grasp your fit for the role.
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 don’t miss out on any important updates. Plus, it shows you’re serious about joining 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 role demands a solid understanding of both fields.
✨Showcase Your Research Impact
Prepare to talk about your previous research projects and how they transitioned from theory to production. Highlight any publications you've contributed to, especially in top-tier conferences, and explain the significance of your work in advancing AI for Physics.
✨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 your work that showcase your ability to optimise models for real-world applications.
✨Emphasise Collaboration and Leadership
This role involves mentoring and working with cross-functional teams, so be prepared to share experiences where you've successfully collaborated with others. Discuss how you've led projects or guided junior researchers, as BeyondMath values strategic leadership.