Staff Machine Learning Engineer

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 means working at the intersection of deep tech, applied science, and engineering innovation. We are a truly global organisation with sites in London (UK) San Francisco (CA), Princeton (NJ) & Calgary (Canada) 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.

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.

Building cutting edge AI systems requires a world‑class team and leadership. In this role you will provide that leadership, setting exceptionally high engineering standards and driving projects from prototype through to production deployment. In addition to technical leadership, this role requires people management; you will be dedicated to mentoring and fostering the growth of your team.

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.

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• Establish and maintain exceptionally high standards for code quality, system architecture and ML engineering practices through hands‑on coding and technical review
  
• Create a culture of technical rigour, first‑principles thinking and engineering craftsmanship
  
• 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
  

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• 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
  

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• Provide technical leadership, mentorship and career development for AI engineers
  
• Manage the intake and development of AI residency programme participants
  
• Foster a culture of learning, curiosity and first‑principles problem solving
  
• Build a high‑performing team that can independently tackle complex, novel problems
  

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• 5+ years of professional experience in ML/AI engineering, with at least 2 years in technical leadership or management roles
  
• 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
  
• Experience managing, mentoring or leading technical teams, with a commitment to developing others’ skills and careers
  
• A strong ability to reason about algorithms, system design and ML engineering trade‑offs
  
• 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”
  

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.