Machine Learning Engineer

Orbital uses AI to build data center hardware that outperforms the competition. Our AI simulates materials at the atomic level, tests millions of hardware configurations in the time traditional methods test hundreds and finds optimal designs - not just good ones. The result is shipped hardware with specs incumbents can't match: 1 MW/rack, PUE < 1.04, 12-week lead times.

Every deployment generates field data that improves our AI models. Better models produce better hardware. Better hardware enables more capable AI. The loop is already closed and tightening with each build cycle. We are not just beneficiaries of AI progress, we are a lever on its rate.

Data Centers are where we start, because the market is urgent and the specs are demanding. But the AI-accelerated development process we've built - materials discovery, hardware design, manufacturing optimization - applies to any complex physical system. Data Centers are the proof point, not the ceiling.

We have sites in London, Canada and the USA, building teams across ML research, Product development, Mechanical engineering, and Chemical engineering. If you want to work where AI meets atoms, we'd like to hear from you.

As a Staff Machine Learning 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

• ONE of:
  • 5+ years of professional experience in ML/AI research or engineering.
  • A relevant PhD + 2 years of professional experience in ML/AI research or engineering.
• 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 reason about algorithms, system design, linear algebra, probabilistic concepts and ML engineering trade‑offs
• An ability to debug complex machine learning systems through meticulous attention to detail, testing of…