Location: London (Hybrid)

About Us

We are a pioneering startup, backed by top VCs, driven to reshape the frontiers of physics simulation using foundational AI models.  Our core aim is to transform engineering design, empowering engineers with unprecedented optimization capabilities driven by AI that rivals the accuracy of traditional simulation methods. Driven by a deep belief that AI will transform how products are engineered across automotive, aerospace, energy, and beyond, we're at the forefront of this exciting revolution. Interest in this space is exploding; join us early and help build a solution that will redefine an entire industry.

We are a values-driven startup embracing fearless innovation, intelligent curiosity, collaboration, a customer-centric approach, whilst creating impact with integrity. These core values shape our culture and are fundamental to our hiring decisions. If you thrive in an environment that pushes boundaries, values learning and teamwork, and seeks to deliver transformative solutions we might be the perfect fit.

The Role

As a Machine Learning Research Engineer, you'll dive deep into developing and advancing the AI models that form the heart of our design optimization solution. This is a role focused on pushing the boundaries of what's possible – using, adapting, and creating entirely new ML methodologies to unlock physics simulations with unparalleled speed and accuracy.  Your research will have immediate and transformative impacts on our platform and the real-world design challenges of our development partners.

Responsibilities

• Physics-Focused AI Model Development: Research, design, and evaluate novel machine learning models with a focus on replicating the accuracy of physics-based simulations within the aerodynamic domain, orders of magnitude faster.
• Model Optimization: Improve the accuracy, speed, and robustness of our ML models and analyse the performance on the industrial-scale problems.
• Geometry Representation: Research effective ways to represent geometric design variations for efficient use by machine learning models.
• Integration Focus: Ensure seamless integration of your models into the optimization platform, working closely with software engineers and our development partners to optimise their real-world use.

Essential Requirements

• Education: Master's Degree (PhD preferred) in Computer Science, Machine Learning, or a related quantitative field.
• Research Experience: Proven track record of conducting independent machine learning research, demonstrated through publications, projects, or prior work.
• Programming: Strong python skills and experience with deep learning libraries (TensorFlow/PyTorch/JAX).
• Foundational Knowledge: Deep understanding of machine learning theory, including optimization, generalisation, and various model architectures.
• Communication: Ability to clearly explain complex ML concepts and research findings to both technical and non-technical audiences.

Highly Desirable

• Publications: Demonstrated ability to publish high-quality research in top-tier AI conferences (e.g., NeurIPS, ICML, ICLR, CVPR) or journals (e.g., JMLR, TPAMI, Artificial Intelligence, Nature Machine Intelligence).