Machine Learning Research Engineer

Orbital Industries is an AI Industrial company, with frontier AI embedded at every step in the production of critical physical products - from creating advanced materials to engineering and manufacturing.

Every Orbital Industries product is developed using CurieOS, our AI operating system, uniting AI-automated hardware engineering with AI-designed material science to achieve breakthrough real-world performance. We also offer CurieOS to our customers and partners, extending the same platform and workflows that power Orbital Industries to their own teams and products.

We have an ambitious mission and need excellent people in all our teams - AI research, operations, advanced materials, mechanical engineering, chemical engineering and manufacturing.

Working at Orbital Industries means working in vertically integrated teams across the full stack, from molecules to manufacturing. We're looking for people who have a love of physical technology, curiosity in AI and a desire to learn.

As a Machine Learning Research Engineer at Orbital, you will architect cutting-edge AI systems for the multi-scale design of physical technologies. When we say multi-scale, we mean it: we build world-class foundation models for simulating both the microscopic motion of atoms and the macroscopic flow of liquids in 1GW data centers. We then co-design across these different scales using the ingenuity of our scientists and engineers, augmented with best-in-class domain agents.

In this role you will set exceptionally high technical standards and drive projects from prototype through to production deployment. First and foremost, we want to work with someone with a love of craftsmanship, continual learning, and building systems that scale. We also value low ego, and a genuine passion for using AI to solve major global industrial technology challenges.

• Establish and maintain exceptionally high standards for code quality, system architecture and ML research and engineering practices through hands-on coding and technical review
• Design robust, well-engineered systems that others can build upon, balancing research velocity with production requirements
• Drive technical decisions on model selection, training approaches and deployment strategies

• Develop and deploy AI solutions across the entire technology development pipeline- computational chemistry simulations, agentic workflows and beyond
• Rapidly upskill in new technical areas through close collaboration with domain experts (no prior chemistry or materials experience required)
• Demonstrate strong implementation skills through hands-on development, contributing significantly to the codebase
• Balance research rigour with pragmatic engineering to deliver production-ready systems at scale

• Design and implement novel ML architectures for complex scientific domains, with work that meets publication standards at top-tier conferences
• Drive research projects from conception through to deployment, showing initiative and technical depth
• Engage continuously with the latest ML literature, staying current with developments in foundation models, generative AI and scientific machine learning

• Significant software engineering and ML experience, with depth in training, evaluating and deploying AI models - demonstrated through industry work
• Proven experience training, evaluating and product ionising AI models at scale, with deep understanding of the full ML lifecycle from research to deployment
• Strong engineering fundamentals with the ability to write high-quality, maintainable code and architect robust systems
• A strong ability to…