Orbital’s hardware solutions solve the biggest challenges in data centers. Each of our products contains an advanced material discovered with our AI, giving it breakthrough real-world performance. We’re looking for ambitious thinkers and builders; those excited by the challenge of bridging AI and industry, chemistry and computation, discovery and deployment.

Our mission is to usher in an era where new, advanced materials are both discovered and engineered with AI and we’ll use this to build hardware that solve major global technology challenges.

Joining Orbital Materials means working at the intersection of deep tech, applied science, and engineering innovation. We are a truly global organisation with teams in London, UK, San Francisco (CA) & Princeton (NJ), building teams in ML Research and Product Development to Mechanical and Chemical Engineering, offering opportunities for talented individuals who want to shape the future of materials and their applications in high-impact industries.

Role Overview

As a Machine Learning Researcher at Orbital, you'll work on world-class foundation models for atomistic systems, pushing the boundaries of AI-driven materials discovery. You'll design and implement cutting-edge ML architectures whilst collaborating closely with experimental scientists to translate foundational research into breakthrough real-world applications. This role offers the opportunity to pursue ambitious, high-quality research that directly accelerates our mission to solve major global technology challenges through AI-discovered advanced materials and beyond.

Key Responsibilities

Independently design, implement and benchmark high-performance ML models

• Develop novel deep learning architectures for complex scientific domains, with work that meets publication standards at top-tier conferences

• Demonstrate strong implementation skills through hands-on development in PyTorch and related frameworks

• Drive research projects from conception through to deployment, showing initiative and technical depth

Coordinate with cross-functional teams, rapidly acquiring relevant domain knowledge

• Collaborate effectively with computational and experimental scientists, translating domain requirements into ML solutions

• Demonstrate intellectual curiosity and the ability to quickly master new scientific domains outside your immediate expertise

• Bridge the gap between foundational ML research and practical applications in materials science

Prototype and iterate rapidly whilst maintaining rigorous engineering standards

• Build robust, well-engineered codebases that others can build upon, balancing research velocity with code quality

• Engage continuously with the latest ML literature, staying current with developments in foundation models, generative AI and scientific machine learning

• Contribute to establishing technical best practices and knowledge sharing within the research team

What We're Looking For

• PhD or 3-5 years of experience in machine learning research

• A strong ability to reason about algorithms, data structures, linear algebra and probabilistic concepts

• A strong ability to debug complex machine learning systems through meticulous attention to detail, testing of edge cases and carefully selected ablations

• An appreciation for high-quality code and software engineering practices

• A genuine interest in pursuing AI-driven scientific discovery and building computational tools that enable breakthrough materials research

• Upon reading Hamming's You and Your Research, you're inspired by quotes such as:

  • "Yes, I would like to do first-class work"

  • "Great scientists … will go forward under incredible circumstances; they think and continue to think"

  • "The more you know, the more you learn; the more you learn, the more you can do; the more you can do, the more the opportunity - it is very much like compound interest"

  • "You should do your job in such a fashion that others can build on top of it, so they will indeed say, 'Yes, I've stood on so and so's shoulders and I saw further.' The essence of science is cumulative. By changing a problem slightly you can often do great work rather than merely good work. Instead of attacking isolated problems, I made the resolution that I would never again solve an isolated problem except as characteristic of a class"

Bonus: Experience working with graph neural networks, message passing neural networks, or computational methods on graphs, 3D point clouds, or other 3D data. Experience training, evaluating or building with large language models.

Why Join Orbital?

• Competitive salary commensurate with AI sector

• Flexible and generous paid time off

• Excellent health, dental and vision insurance plan

• Equity package - the ability to own part of Orbital Materials as we grow

• Regular company offsites to the USA and beyond

• The experience of working in a cutting-edge organisation dedicated to a better future

• Clear pathways to technical leadership and ownership of research domains as we scale

Orbital Materials is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.