Orbitalindustries
Machine Learning Engineer
4mo ago
EuropeRemotemachine learningaihardware engineering
Architect cutting-edge AI systems for the multi-scale design of physical technologies in an AI-first industrial company.
Requirements
- Significant software engineering and ML experience, with depth in training, evaluating and deploying AI models - demonstrated through industry work
- Proven experience training, evaluating and productionising 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 edge cases and carefully selected ablations
- A genuine interest in building AI systems that enable breakthrough scientific and industrial applications
- Upon reading Hamming's You and Your Research, you resonate with quotes such as: "Yes, I would like to do first-class work"
- "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.'"
- "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 with physics-informed or chemistry-focused AI applications. Experience building or fine-tuning large language models. Experience with agent-based systems, tool use or agentic workflows. Contributions to open-source ML projects or published research.
- Orbital is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
Other
- Orbital is an AI-first industrial company building hardware from the atoms up. Our goal is to lead an industrial renaissance to advance critical technologies and secure our planet for generations to come.
- We’re starting with critical hardware for AI data centers to make them more performant and sustainable. Every Orbital product is invented with our AI platform — uniting AI-automated hardware engineering with AI-designed material science to achieve breakthrough real-world performance.
- 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 means working in tightly integrated, vertically integrated teams. We’re looking for people who have a love of physical technology, curiosity in AI and a desire to learn.
- As a 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